The Critical Success Factors of Data Warehousing

Applications

By Majdi AbuSaleem

Master’s Thesis in Accounting Swedish School of Economics and Business Administration

2005

II

Hanken- Swedish School of Economics and Business Administration

Department: Accounting Type of Work: Master of Science Thesis Author: Majdi AbuSaleem Date: 03.11.2005 Title of Thesis: CRITICAL SUCCESS FACTORS OF DATA WAREHOUSING

APPLICATIONS: THE CASE OF FINNISH COMPANIES. Abstract: The purpose of this thesis has been to investigate the Critical Success Factors (CSFs),

under the organizational, environmental and project-related dimensions, which influence the adoption of data warehouse technologies in the Finnish market.

In the theoretical portion ERP and Data warehouse implementation and success factors

literature have been reviewed and discussed within the context of Critical Success Factors of data warehousing.

The subject of the empirical research has been to explore the impact of the selected

factors, under organizational, project-related and environmental dimensions, on data warehouse applications in Finnish companies. A focused survey was conducted among mid to large-sized Finnish companies crossing many industrial classifications.

A total of 220 questionnaires were e-mailed to targeted people at selected companies.

Eighteen responses to the questionnaire were received after a period of more than two months. The results revealed that all organizational and project-related factors, in addition to one factor under the environmental (Selection of vendors) dimension would affect the adoption of data warehouses in Finnish companies.

Keywords: Critical Success Factors (CSF), Data warehouse technology,

Organizational dimension, Project-related dimension, Environmental dimension, mid- and large-sized companies, Selection of vendors.

III

Table of contents

1. INTRODUCTION: ....................................................................................................... 1

1.1 BACKGROUND TO THE THESIS:........................................................................... 1

1.2 THESIS OBJECTIVES:........................................................................................... 2

1.3 STRUCTURE OF THE THESIS:............................................................................... 3

2. DATA WAREHOUSE.................................................................................................. 5

2.1 OBJECTIVE AND STRUCTURE.............................................................................. 5

2.2 DATA WAREHOUSE DEFINITION ......................................................................... 5

2.3 BEFORE DATA WAREHOUSING APPLICATIONS ................................................... 7

2.3.1 Data from legacy systems ............................................................................. 7

2.3.2 Desktop-based applications.......................................................................... 8

2.3.3 Decision support and executive information systems ................................ 8

2.3.4 Key factors for data warehouse emergence................................................ 9

2.4 DATA WAREHOUSING CONCEPTS AND CHARACTERISTICS .............................. 10

2.4.1 key features of data warehouse.................................................................. 10

2.4.2 Difference between operational systems and data warehouses .............. 11

2.5 SUMMARY OF THE CHAPTER ............................................................................ 14

3. CRITICAL SUCCESS FACTORS OF ERP IMPLEMENTATION..................... 15

3.1 OBJECTIVE AND STRUCTURE............................................................................ 15

3.2 ERP DEFINITION & CHARACTERISTICS ........................................................... 15

3.3 Prior relevant studies and research papers .............................................. 17

3.4 DEFINITION OF FACTORS INFLUENCE THE ERP IMPLEMENTATION ............... 25

3.5 SUMMARY OF THE CHAPTER ............................................................................ 34

4. CRITICAL SUCCESS FACTORS OF DATA WAREHOUSE

IMPLEMENTATION .................................................................................................... 35

4.1 OBJECTIVE AND STRUCTURE ........................................................................... 35

4.2 PRIOR RELEVANT STUDIES AND RESEARCH PAPERS ........................................ 35

4.2.1 (Joshi and Curtis, 1999).............................................................................. 39

4.2.2 (Wixom and Watson, 2001) ........................................................................ 39

IV

4.2.3 (Hwang et al. 2004) ..................................................................................... 40

4.2.4 (Mukherjee and D’Souza, 2003) ................................................................ 41

4.2.5 (Solomon, 2005) ........................................................................................... 41

4.2.6 (Hurley and Harris, 1997) .......................................................................... 42

4.2.7 (Watson et al. 2002)..................................................................................... 42

4.3 DEFINITION OF FACTORS INFLUENCING THE DATA WAREHOUSE

IMPLEMENTATION ........................................................................................................ 43

4.3.1 Organizational factors: ............................................................................... 43

4.3.2 Environmental factors: ............................................................................... 46

4.3.3 Project-related factors: ............................................................................... 47

4.3.4 Technical factors:........................................................................................ 49

4.3.5 Educational factors:.................................................................................... 50

4.4 CLASSIFYING THE CSF BASED ON THE PHASED LOGIC OF THE DATA

WAREHOUSE IMPLEMENTATION ................................................................................ 52

4.4.1 Pre-implementation phase.......................................................................... 52

4.4.2 Implementation phase ................................................................................ 54

4.5.1 Post-implementation phase Factors .......................................................... 55

4.5 FACTORS INVESTIGATED IN THE THESIS.......................................................... 58

4.5.1 Organizational factors: ............................................................................... 58

4.5.2 Environmental factors: ............................................................................... 59

4.5.3 Project-related factors: ............................................................................... 59

4.6 SUMMARY OF THE CHAPTER ............................................................................ 63

5. EMPIRICAL RESEARCH........................................................................................ 64

5.1 OBJECTIVE AND STRUCTURE............................................................................ 64

5.2 RESEARCH PROBLEM AND OBJECTIVES ........................................................... 64

5.3 RESEARCH MODEL............................................................................................ 64

5.4 HYPOTHESES AND VARIABLES ......................................................................... 66

5.4.1 Organizational dimension .......................................................................... 66

5.4.2 Environmental dimension .......................................................................... 68

5.4.3 Project-related dimension .......................................................................... 69

5.5 DATA COLLECTION........................................................................................... 71

V

5.5.1 Questionnaire ............................................................................................... 71

5.6 DATA ANALYSIS AND DISCUSSION OF RESEARCH RESULTS.............................. 73

5.6.1 Analysis of data gained via questionnaire ................................................. 73

5.7 GENERAL ANALYSES......................................................................................... 96

5.7.1 Product profitability ...................................................................................... 96

5.7.2 Customer profitability ................................................................................... 97

5.7.3 Employee profitability ................................................................................... 99

5.7.4 Branch profitability ..................................................................................... 100

5.7.5 Productivity .................................................................................................. 102

5.7.6 Customer satisfaction .................................................................................. 103

5.7.7 List of critical success factors and Discussion of Observations:........... 105

5.8 SUMMARY OF THE CHAPTER .......................................................................... 115

6. CONCLUSION ......................................................................................................... 117

6.1 OBJECTIVE AND STRUCTURE.......................................................................... 117

6.2 GENERAL CONCLUSIONS................................................................................ 117

6.2.1 Conclusions about the critical success factors of data warehousing in the

Finnish companies................................................................................................. 117

6.2.2 Conclusions about the benefits obtained from installing data warehouse

applications ............................................................................................................ 119

6.2.3 Conclusions about the current status related to the adoption of data

warehouse technology ........................................................................................... 120

6.3 VALIDITY, RELIABILITY AND GENERALIZABILITY ........................................ 121

6.4 IMPLICATIONS FOR FURTHER RESEARCH ...................................................... 121

REFERENCES............................................................................................................. 123

APPENDIX................................................................................................................... 125

VI

List of Figures:

FIGURE 4.1: ASSIGNING THE CSF INTO DW PHASES............................................. 56

FIGURE 4.2: NUMBER OF RESEARCHES ON CSF .................................................... 62

FIGURE 5.1: RESEARCH MODEL ............................................................................... 66

FIGURE 5.2: RESPONDENT’S TITLE OF POST DISTRIBUTION.............................. 75

FIGURE 5.3: INDUSTRY DISTRIBUTION ................................................................... 77

FIGURE 5.4: VENDOR DISTRIBUTION....................................................................... 79

FIGURE 5.5: DW TYPE DISTRIBUTION........................................................................ 80

FIGURE 5.6: DW COMPLEXITY DISTRIBUTION ...................................................... 81

FIGURE 5.7: EXISTENCE OF CHAMPION IMPORTANCE DISTRIBUTION ........... 83

FIGURE 5.8: TOP MANAGEMENT SUPPORT IMPORTANCE DISTRIBUTION ...... 84

FIGURE 5.9: BUSINESS NEEDS IMPORTANCE DISTRIBUTION ............................. 85

FIGURE 5.10: COMPATIBILITY WITH PARTNER IMPORTANCE DISTRIBUTION

........................................................................................................................................... 88

FIGURE 5.11: BUSINESS COMPETITION IMPORTANCE DISTRIBUTION............. 90

FIGURE 5.12: PROJECT TEAM SKILLS IMPORTANCE DISTRIBUTION ............... 91

FIGURE 5.13: ORGANIZATIONAL RESOURCES IMPORTANCE DISTRIBUTION 92

FIGURE 5.14: SUPPORT FROM OUTSIDE CONSULTANT IMPORTANCE

DISTRIBUTION ............................................................................................................... 94

FIGURE 5.15: END-USER INVOLVEMENT IMPORTANCE DISTRIBUTION .......... 95

FIGURE 5.16: IMPORTNCE DISTRIBUTION OF DW IN PRODUCT

PROFITABILITY ............................................................................................................. 96

FIGURE 5.17: IMPORTANCE DISTRIBUTION OF DW IN CUSTOMER

PROFITABILITY ............................................................................................................. 98

VII

FIGURE 5.18: IMPORTANCE DISTRIBUTION OF DW IN EMPLOYEE

PROFITABILITY ............................................................................................................. 99

FIGURE 5.19: IMPORTANCE DISTRIBUTION OF DW IN BRANCH

PROFITABILITY ........................................................................................................... 101

FIGURE 5.20: IMPORTANCE DISTRIBUTION OF DW IN PRODUCTIVITY ......... 102

FIGURE 5.21: IMPORTANCE DISTRIBUTION OF DW IN CUSTOMER

SATISFACTION ............................................................................................................. 104

VIII

List of tables:

TABLE 2.1: DIFFERENCE IN FEATURES BETWEEN. OLAP AND OLTP ................ 14

TABLE 3.1: RESEARCH PAPERS ABOUT CSF IN ERP SYSTEMS ............................ 20

TABLE 4.1: RESEARCH PAPERS ABOUT CSF IN DW SYSTEMS ............................. 39

TABLE 4.2: ASSIGNING THE CSF INTO DW PHASES............................................... 58

TABLE 4.3: DW AND ERP RESEARCHES THAT DISCUSSED THE INVESTIGATED

FACTORS ......................................................................................................................... 58

TABLE 5.1: SIZE OF THE COMPANY DISTRIBUTION.............................................. 76

TABLE 5.2: INDUSTRY DISTRIBUTION ..................................................................... 77

TABLE 5.3: DW’S YEAR OF INSTALLATION DISTRIBUTION................................. 78

TABLE 5.4: RANKED LIST OF CSF............................................................................ 105

TABLE 5.5: CROSS-TAB TABLE 1.............................................................................. 112

TABLE 5.6: CROSS-TAB TABLE 2.............................................................................. 113

TABLE 5.7: CROSS-TAB TABLE 3.............................................................................. 114

TABLE 5.8: CROSS-TAB TABLE 4.............................................................................. 115

1

1. Introduction:

1.1 Background to the thesis:

It is a critical aspect for organizations in today’s highly globalized

market, to manage transaction- and non-transaction- oriented information

for making timely decisions and respond to changing business

circumstances. With the receding economy, enterprises have changed

their business focus towards customer orientation to remain competitive.

Accordingly, maintaining relationships with clients and managing their

data have appeared as top issues to be considered by global companies.

Many researchers have reported that the amount of data in a given

organization doubles every five years. (www.ciol.com)

The most fundamental aspect in a particular organization is the critical

decision making capacity of the management, which influence the

successful running of business operations.

For such decisions, the information must be reliable, accurate, real-time

and easy-to-access. For such information, all the enterprise-related data

should be appropriately analyzed from a multi-dimensional point of view

and presented at one place. The solution is a data warehouse!

A data warehouse is one of the fundamentals of the decision support

systems that are used to support the decision making initiatives, of many

IS technologies.

Since the introduction of the term “data warehousing” in early 1990s,

companies have investigated the ways they can capture, store and

manipulate data for analysis and decision support (Smith, TDAN.com).

2

As indicated by market surveys of data warehouse technology, the

worldwide need of data warehousing solutions has grown greatly in the

last 5 years.

The US market share alone accounted for $72.7 billion worth of data

warehousing solution sales by 2003. The US market is growing by 41%

annually.(www.ciol.com)

A data warehouse is not only a software package. The adoption of data

warehouse technology requires massive capital expenditure and a certain

deal of implementation time. Furthermore it has a high likelihood of

failure.

It is crucial to have a thorough understanding of critical success factors to

assure the successful embracing of data warehouse technology.

Former research papers have focused on technical, data related,

operational and educational matters of data warehouse implementation.

There is an obvious lack of theoretical and empirical studies which

discuss organizational, project-related and environmental dimensions

regarding adoption of data warehouse technology in general and in

Finnish companies in particular. This study will address important

concerns and attract attention of data warehouse researchers, because it

empirically investigates critical factors under organizational,

environmental and project dimensions in Finnish companies.

1.2 Thesis objectives:

Data warehouse technology is a very costly, time-consuming and risky project

compared with other Information technology initiatives, as cited by prior

researchers (Wixom and Watson, 2001), (Hwang et al, 2004), (Mukherjee and

D’Souza, 2003), (Solomon, 2005), and (Watson et al. 2002).

Therefore it is important to have a deeper understanding about the factors

which affect the adoption of data warehouse technologies.

3

The research problem of this thesis can be portrayed as “what are the Critical

Success Factors under organizational, environmental, and project-related

dimensions that influence the process of adopting data warehouse technology

in Finnish companies”.

1.3 Structure of the thesis: The structure of this thesis follows the standardized pattern of scientific

research papers. I start by presenting an executive summary of the overall

thesis. The first chapter of the thesis is Introduction, where I present the

thesis background, then define the goals and the objectives of the thesis.

A brief tour will be held through the second chapter regarding data

warehouse definition, old applications that were used before the

introduction of the data warehouse, and the concepts and common

characteristics of data warehouses.

These aspects are introduced in order to reveal the complexity of data

warehouse technology and the importance of having a thorough

knowledge and awareness of all aspects regarding data warehouse. This

will lead to the increased possibility of having a successful data

warehouse implementation.

The third chapter opens a discussion about Critical Success Factors

influencing the adoption of ERP systems. During the last decade, ERP

has attracted the attention of practitioners and academics due to its impact

on managing facets of business and integrating enterprise functions.

This chapter was included in the thesis for the following reasons:

• A lot of researches have targeted different aspects of ERP

systems, particularly the CSFs aspect.

• The lack of sufficient theoretical and empirical research on CSFs

in data warehouse implementation.

4

This chapter served the study more from the background information

point of view. It defines the critical issues and explores their influence on

data warehouse technology.

The first three chapters represent the entrance to the fourth chapter.

Chapter four begins to discuss the main objective of the thesis by

providing the reader with comprehensive insights about critical success

factors which influence the adoption of data warehouse technology, based

on the findings of prior research papers.

Chapter five talks about the empirical side of the thesis and encompasses

development of hypotheses, methodology used in this study (the ways of

collecting and gathering the data) and description of the sample. In this

chapter the proposed hypotheses are tested, then the data gathered from

the methodology is analyzed and discussed.

The conclusions and the suggestions for further research are introduced in

chapter six.

Appendix and References are presented at the end of the thesis.

5

2. Data warehouse

2.1 Objective and structure

In this chapter, Data warehouse technology is introduced to the reader to

assemble a preliminary knowledge pertaining to its definition, its

characteristics and its contribution to maximizing the performance of

adopters.

Section 2.2 defines the data warehouse from the point of view of the so-called

data warehouse leaders and argues their definitions. In section 2.3, the

historical techniques of data analysis, reporting and querying before the

emergence of data warehousing are presented. The key reasons that led to the

invention of data warehousing are then cited. Data warehouse concepts and

common characteristics are discussed in section 2.4.

This chapter is built based on reviewing the following reference material:

(Kimball, 1996), (Hwang et al. 2004), (Inmon, 1996), (Gupta, 1997), (Han

and Kamber, 2000), (Todman, 2001) (Hashmi, 2000), and (A. Smith,

TDAN.com).

2.2 Data warehouse definition Early constructers of data warehousing technologies considered their products

to be the key components of future IT strategy and architecture since the

introduction of the term "data warehousing" in late eighties and early nineties.

At that time companies explored the ways to capture, store and manipulate

data for analysis, reporting and decision making initiatives. Data warehousing

has quickly evolved into a distinctive and popular business application class.

Numerous examples of highly successful implantation of data warehousing

applications can be cited from different fields and sizes of business.

6

Nowadays this simple concept becomes a multibillion-dollar industry, and

both practitioners and academicians believe that data warehousing

applications are up-to-the-minute weapons for decision-making initiatives.

(Hashmi, 2000)

Ralph Kimball defined in his book “The Data Warehouse Toolkit” a data

warehouse:

A copy of transaction data specifically structured for query and analysis

(Kimball, 1996).

I have two slight criticisms of his definition:

1. You can sometimes find non-transactional data stored in a data warehouse.

2. Data warehouses are used heavily for querying and reporting initiatives

rather than for querying and analysis activities. Querying and analysis are two

faces to the same coin.

(Hwang et al. 2004) in their article “critical factors influencing the adoption

of data warehouse technology” introduced a thorough definition of a data

warehouse:

An application collects daily transaction-oriented enterprise data both

internally and externally and then accumulates, categorizes, and stores huge

historical data for further analysis, prediction and discovery of data pattern

(Hwang et al, 2004). They added:

Those data are more related to statistics, non-modified and stored in

warehouse in a long-term manner, also they are time-oriented, integrated and

can be used effectively for analyzing and decision-making (Hwang et al,

2004).

The authors defined a data warehouse as a transaction-oriented data repository

(as Kimball stated in his definition). In reality, data warehouse can store

transactional and non-transactional data.

7

This study adapts the definition of William’s H. Inmon, who is known as the

father of data warehousing, from his famous, book “Building the data

warehouse”:

A subject-oriented, integrated, non-volatile and time-variant collection of data

in support of management decisions (Inmon, 1996).

A closer look will be taken at each of the above-mentioned key features in

Inmon’s definition in the data warehousing concepts and characteristics

section.

2.3 Before data warehousing applications In this section a brief tour will be held through the historical ways and

techniques of data analysis, reporting and querying before the invention of the

data warehouse. After that the key factors that have led to the evolution of

data warehousing technologies will be mentioned.

In the past, a crucial stress had been given to operational systems and the data

derived from these systems. It is impractical in any way to keep data forever

in the operational systems. One good reason is that the strategic data supplied

by an analysis system is needed for decision making initiatives, which support

the core competence of the organization.

The fundamental prerequisites for the operational systems and analysis

systems are absolutely different: The operational systems need performance,

whereas the analysis systems need flexibility and broad scope (Gupta, 1997).

This section is divided into four parts to build a comprehensive review

concerning the historical methods and techniques used before introducing the

data warehouse.

2.3.1 Data from legacy systems

8

In the 1970’s until the late 1980’s business applications were run in a

mainframe-based environment using different software platforms (Cobol,

IMS, DB2) (AS/400 and VAX/VMS) (Gupta, 1997).

Although the introduction of the client-server was in the late eighties, the

heavy weight of business data still resided in the mainframe environment.

This was due to the ability of these systems to catch and process business

knowledge and rules that were too difficult to be managed effectively by a

new application or platform at that time.

These systems were called legacy systems, which were considered the main

source for data analysis.

2.3.2 Desktop-based applications

During the past decade, the world has experienced a radical increase in

demand for desktop-based applications due to the wide popularity of personal

computers. Desktop tools and programs increasingly targeted toward the end

users. These tools and programs have introduced new techniques for business

analysis and blurred the gap between programmers and end users.

Desktop tools and programs generate data geared toward very specific needs

and desires. In other words the user can get what he or she wants, and the

extracted data can not address the requirements of multiple users and uses.

Desktop tools and programs are too expensive and time-consuming because

they are user-specific tools.

2.3.3 Decision support and executive information systems

Decision support systems provide aggregated information for lower or mid-

level managers. Executive information systems provide a lower level of

aggregated information with multi-dimensional capabilities, which are

targeted toward high level executives due to their need to slice and dice the

data for strategic decision making (Gupta, 1997).

9

Decision support and executive information systems are designed to be used

by non-technical users; this could be the key reason behind the development

of these systems.

2.3.4 Key factors for data warehouse emergence

As mentioned by Gupta in his study (An introduction to data warehousing),

two aspects have led to the appearance of data warehousing, technical matters

and business matters (Gupta, 1997).

The discussion below is a short précis of his outlook regarding the reasons of

data warehouse appearance.

2.3.4.1 Technical matters

1. The sharply increasing power of hardware coupled with its falling price

has led to the introduction of more powerful data analysis tools in business.

2. Increasing the power of desktop software and hardware: Personal

computers, in the past, were used for minor tasks such as word processing.

After the introduction of powerful desktop software and hardware, the

personal computer has become the main tool for powerful multi-dimensional

analysis and has allowed the maturation of client-server environment.

3. Evolution of server software:

Server operating systems and software have become feature-rich with multi-

tasking and multi-processing capabilities. This software is available in an

inexpensive manner.

4. Emergence of intranets and web-based applications:

Internet and web-based tools are heavily used in data warehousing

applications. These technologies enable the data warehouse to work 24 hours

a day in inexpensive fashion, in addition to providing a multi-tier basis where

all the heavy-duty analysis takes place before the data is presented to end

users.

5. Data access-tools crisis:

10

Every day an organization generates billions of bytes of data about various

aspects of operation such as customers, products, operations and people.

Small portions of data are caught, processed and stored for executives and

decision makers. The remainders are locked up in the information system; this

phenomenon is called “data in jail”.

2.3.4.2 Business matters

1. Economic factors:

In recent years economic factors have changed the way in which the

organizations incorporate and pushed them to re-evaluate their business

practices.

2. Globalization:

The common trend for companies, to be a global corporation, forces

companies to incorporate efficiently and effectively. This is tied with

possessing powerful analysis tools.

3. End users become more knowledgeable in technical aspects:

Day by day, the users become more proficient in technical matters.

Technology-savvy end users have played an integral role in the development

of the data warehouse and other powerful data processing technologies, since

they are the main users of such kinds of technologies.

2.4 Data warehousing concepts and characteristics This section explores data warehousing concepts and characteristics. These

concepts and characteristics are grouped into two Sub-sections.

2.4.1 key features of the data warehouse

W. H. Inmon, a chief architect in data warehouse construction, defines a data

warehouse as: a subject-oriented, integrated, non-volatile and time-variant

collection of data in support of management decision making (Inmon, 1996).

11

Let’s take a closer look at the four keywords in Inmon’s definition, based on

reviewing the relevant books and research (Inmon, 1996), (Han and Kamber,

2000), (Todman, 2001) and (Hashmi, 2000):

• Subject-oriented: A Data warehouse is organized around key subjects such

as customer, supplier, and sales, this enables the data warehouse to provide a

concise view of these subjects.

• Integrated: A data warehouse is constructed by integrating data from varied,

heterogeneous databases and information systems such as relational

databases and flat files.

• Time-variant: A data warehouse stores historical data and covers a much

longer time horizon than any other data repository (several years to decades);

the time element is included implicitly or explicitly in every key structure in a

data warehouse.

• Non-volatile: A data warehouse contains read-only data, which are updated

in planned periodic cycles not frequently, so once the data is stored in a data

warehouse it is not easily changed.

2.4.2 Difference between operational systems and data warehouses

There are two fundamentally different types of information systems in all

organizations, as cited by many researchers (Gupta, 1997), (Han and Kamber,

2000), (Todman, 2001) and (A. Smith, TDAN.com):

• Operational systems: Systems that support day-to-day operations, such as

order entry, inventory, accounting and payroll systems. Organizations cannot

operate without their operational systems and the data that these systems

maintain.

12

• Informational systems: These systems are used for planning, forecasting and

managing the organization, such as marketing planning and financial analysis,

which support data analysis and decision-making.

Online operational systems, which are called online transaction processing

(OLTP), perform online transaction querying and processing. On the other

hand data warehouses, which are considered one of the informational systems,

is based on online analytical processing (OLAP). OLAP technologies serve

the knowledge-workers in the role of data analysis and decision making.

A Data warehouse is constructed separately from operational systems. The

main reason for such a separation is the potential degradation of the

operational systems, which can result from the analysis process, and to

promote the high performance of both systems, as mentioned by (Han and

Kamber, 2000).

They added the following other reasons for the separation between a data

warehouse and operational systems:

Firstly, an operational system is designed from known tasks and workloads,

such as indexing and hashing using primary keys, searching for particular

records and optimizing canned queries. On the other hand, data warehouse

queries are often complex. They involve the computation of large groups of

data at the summarized level and may require the use of special data

organization, access, and implementation methods based on a

multidimensional view.

Secondly, an operational database supports the concurrent processing of

multiple transactions. Concurrency control and recovery mechanisms such as

locking and logging are required to ensure the consistency and robustness of

transactions. OLAP query often needs read-only access of data records for

summarization and aggregation.

13

So applying any one of them to do the other’s mission may degrade the

performance of the system.

Thirdly, there are major distinction characteristics between operational system

(OLTP) and data warehouse (OLAP). The table below indicates the

differences in characteristics between the both systems, as shown and

discussed by (Han and Kamber, 2000)

Feature OLTP OLAP

Characteristic Operational processing Informational processing

Orientation Transaction Analysis

User Clerk, DBA, database

professional

Knowledge worker

Function Day-to-day operations Long-term informational

requirements, decision

support

DB design ER based, application

oriented

Star/snowflake, subject

oriented

Data Current; guaranteed up-to-

date

Historical; accuracy

maintained over time

Summarization Primitive, highly detailed Summarized, consolidated

View Detailed, flat relational Summarized,

multidimensional

Unit of work Short, simple transaction Complex query

Access Read/write Mostly read

Focus Data in Information out

Operations Index/hash on primary key Lots of scans

Number of records

accessed

Tens Millions

Number of users Thousands Hundred

14

DB size 100 MB to GB 100 GB to TB

Priority High performance, high

availability

High flexibility, end-user

autonomy

Metric Transaction throughput Query throughput,

response time

Table 2.1

2.5 Summary of the chapter Data warehouses have become one of the most talked about information

technologies for today’s business. Although the term of data warehousing was

coined in the early nineties, the global trend is headed for accommodating this

technology due to myriad benefits acquired by the adopters.

Many reasons have contributed to emergence of data warehousing (as cited

previously) in the business field. The lack of convenient awareness, in regard

of data warehousing in general and critical success factors in particular, has

raised a barrier in front of the adopters.

Data warehouses are different from operational systems (see the table in

section 2.4.2). Therefore, the separation between data warehouse and

operational systems is a must to promote the high performance of both

systems.

This chapter provides the reader with a preliminary insight about data

warehouses in order to progress toward the investigation of the critical issues

impacting data warehouse applications.

15

3. Critical success factors of ERP implementation

3.1 Objective and structure

This chapter proposes to exhibit the critical success issues influencing ERP

implementation processes and discusses them from point of view of

practitioners and academics. The reason for including this chapter in the thesis

is to define the critical success issues in ERP, which were discussed largely in

the literature and empirical research, and explore their impact on data

warehouse implementation projects, which suffer from the lack of related

literature and empirical studies that discuss the CSFs in data warehousing.

The ERP system is defined and the common characteristics of this system are

listed in section 3.2. Finally, in section 3.3 the prior relevant research papers

in the field of CSFs of ERP projects are cited and further discussed.

The content of this chapter is based on the following books and research

papers: (O’Leary, 2000), (Mabert et al. 2001), (Nah et al. 2001), (Bingi et al.

1999), (Akkermans and Helden, 2002), (Umble et al. 2003), and (Parr and

Shanks, 2000).

3.2 ERP definition & characteristics Global enterprises around the world have invested heavily in information

technology to take advantage of tackling and altering the myriad challenges

and changes experienced in today's highly competitive market. Many firms

have accommodated company-wide systems called Enterprise Resource

Planning (ERP) systems. ERP systems are designed to integrate different

factional parts of the organization in a unified fashion and optimize coherent

business processes.

16

By the late 1990s, companies were spending over $23 billion a year on

enterprise software of which a major portion was ERP software (Mabert et al.

2001).

What does the term “ERP system” mean?

In his book “Enterprise resource planning systems”, Daniel O’Leary

defined ERP systems as computer-based systems designed to process an

organization’s transactions and facilitate integrated and real-time planning,

production and customer response (O’Leary, 2000).

He listed the following characteristics that an ERP system is assumed to have

(O’Leary, 2000):

• An ERP system is packaged software designed for the client server

environment, i.e. client (user’s computer) and server (other computing

source that provides computing resources such as software and data) are

linked so that the computing and storage can be distributed between the

client and server.

• ERP integrates the majority of a business’s processes.

• ERP processes large majority of an organization’s transactions.

• ERP uses an enterprise-wide database that stores each piece of data once

(but it has limited capabilities compared with those of the data warehouse

in terms of storing historic data, multidimensional view and analysis of

data, data integration from multiple data source and storage size of data)

• ERP allows access to the data in real time.

• Support for multiple currencies and languages

• Support for specific industries, i.e. Different ERP applications for each

field of industry (gas, oil, health care, chemicals and banking).

In 1999, the top five vendors (J.D. Edwards, Baan, Oracle, PeopleSoft, and

SAP) in the ERP market accounted for 59 percent of the industry's revenue.

AMR Research expects the top five vendors in 2005 (SAP, Oracle, Sage

17

Group, Microsoft, and SSA Global) to account for 72 percent of ERP vendors'

total revenue.

The term Enterprise Resource Planning was invented by Gartner Group in the

early 1990s to describe the extended version of MRP II (manufacturing

resource planning). ERP software includes integrated modules for accounting,

finance, sales and distribution, Human resource management, material

management, supply chain management and other business functions (Mabert

et al. 2001).

3.3 Prior relevant studies and research papers ERP systems may well count as the most important development in the

corporate use of information technology in the 1990s’.

ERP projects are usually expensive, complex and risky projects that may take

several years and cost millions of dollars to make the system alive. In addition

to engaging large groups of people and other resources working together

under substantial time stress and facing many sudden and unforeseen

developments, as indicated by prior research (Mabert et al. 2001), (Nah et al.

2001), (Bingi et al. 1999), (Akkermans and Helden, 2002), (Umble et al.

2003), and (Parr and Shanks, 2000).

The aforementioned challenges have created pressure on the academicians’

shoulders to gear their research effort toward investigating the critical success

factors that influence the ERP implementation project.

The table below summarizes from the earlier research papers all the important

factors for enterprises to consider in the process of adopting ERP applications

with a short synopsis about each research paper.

18

Authors Factors About the Paper

Mabert et al. Senior management support,

cross-functional team, defining

the objective and project details,

clear guidelines for

implementations, consultants,

and training users.

An empirical study, which

investigated the importance of an

ERP system, process and procedures

of ERP installation, key success

factors, the improved area after the

implementation and accumulated

benefits from ERP installation.

They interviewed key business

managers and IT professionals in 15

different ERP implementations

(ranging from small to large firms)

and the senior ERP consultants from

6 different consulting firms.

Nah et al. Teamwork and composition, top

management support, business

plan and vision, effective

communication, project

management, project champion,

appropriate business and legacy

systems, change management

program and culture, business

process reengineering and

minimum customization, and

software development,

monitoring and evaluation of

performance.

A theoretical research presented 11

factors (from previous relevant

research studies) emerged as critical

to successful implementation of ERP

systems. These factors were

classified into the appropriate phases

in Markus’s and Tanis’s ERP life

cycle.

19

Bingi et al. Top management commitment,

reengineering, integration, ERP

consultants, Implementation

time, implementation costs, ERP

vendors, selecting right

employees, training employees

and employee morale.

A theoretical study probed for the

critical issues affecting the ERP

implementations based on the

previous researches in this field.

Akkermans

and Helden

Top management support, project

team competence,

interdepartmental co-operation,

clear objectives and goals, project

management, interdepartmental

communication, management of

expectations, project champion,

vendor support and careful

package selection.

The authors deployed a list of 10

critical success factors, which

influence the ERP implementation

and taken from (Toni Somers and

Klara Nelson, 2001), in a specific

company case that adapt ERP

system. The authors aimed, by

including a company case into their

studied, to build a rich framework

and test the explanatory power of the

CSF.

Umble et al. Clear understanding of goals, top

management commitment,

excellent project management,

organizational change

management, great

implementation team, data

accuracy, extensive education

and training, focused

performance measures, and

multi-site issues.

An empirical study presented the

CSFs, software selection steps, and

critical implementation procedures

for successful implementation. A

case study of successful ERP

implementation was launched and

discussed in terms of the above-

mentioned aspects.

Parr and Management support, release The authors defined 3 phases of ERP

20

Shanks full-time relevant business

experts, empowered decision

makers, set realistic milestones

and end date, champion,

minimum customization, smaller

scope, definition of goals and

scopes, balanced team, and

commitment to change.

implementation project. After that

they introduced the CSFs of ERP

system in each phase of the ERP

project.

Two case studies were presented at

the same company. The first one is an

unsuccessful implementation of ERP

project and the second one is a later

successful implementation.

Table 3.1

The argument below provides a quick trip for the reader through the prior

relevant research papers.

3.3.1 (Mabert et al, 2001)

They presented in their paper an objective and comprehensive insight of ERP

systems as a management tool for coordinating the activities of a firm.

They started with defining ERP clearly and highlighting its advantages and

disadvantages, then moved to the process of selecting and installing the ERP

system. After that they introduced the key prerequisites for an ERP

implementation project (required resources). Later on they underscored the

accumulated benefits gained from the ERP implementation project by the

adaptor. Finally the key success factors for ERP system were counted and

explained.

Their methodology was based on conducting interviews in 15 different ERP

implementations with key business managers and IT professionals. Although

the sample was limited, it included varied firms (small to large) with diverse

industrial and consumer products. They also interviewed the senior ERP

consultants from 6 different consulting firms. The data from these interviews

21

were used to answer the questions that were presented in the research, using

professional insights in the area of ERP systems.

3.3.2 (Nah et al, 2001)

They introduced 11 factors that were found to be critical to ERP

implementation success. They have a distinctive way of introducing these

factors in their research, by classifying these factors into respective phases

(chartering, project, shakedown, onward and upward). These phases were

derived from Markus and Tanis’ ERP life cycle model. The importance of

each factor and its contribution in each phase were discussed.

Through an intensive review of the literature, they found ten articles that

provide the answer to the following question: What are the key critical factors

for ERP implementation success?

These articles were defined through a computer search of databases of

published works and conferences in information systems area in general and

ERP systems in particular.

The following is their list of key factors that affect ERP implementation

success:

1. ERP teamwork and composition.

2. Change management program and culture.

3. Top management support.

4. Business plan and vision.

5. Business process reengineering with minimum customization.

6. Project management.

7. Monitoring and evaluation of performance.

8. Effective communication.

9. Software development, testing and troubleshooting.

10. Project champion.

11. Appropriate business and IT legacy systems.

22

3.3.3 (Bingi et al. 1999)

Bingi et al. promoted the critical implementation concerns that must be

understood by ERP adopters for ongoing ERP implementation success.

They started the research with giving an adequate overview of ERP solutions

(their definitions, their importance and contribution, and their advantages and

disadvantages). Then they identified and discussed the critical issues in ERP

implementation.

Top management, Reengineering, Integration, ERP consultants,

Implementation time, Implementation costs, ERP vendors, Selecting the right

employees, Training employees, and Employee morale are critical issues that

must be realized by the organizations when ERP system is seriously

undertaken, according to researchers’ points of view.

Based on reviewing previous related-literature and relevant field-experience,

the researchers built their research model and discussed the issues that are

critical for successful ERP implementation projects.

3.3.4 (Akkermans and Helden, 2002)

Akkermans and Helden listed out a group of critical factors for successful

implementation of ERP systems. This list was used to analyze and explain the

project performance in one ERP implementation project at a company in the

aviation industry.

In the case study, poor project performance lead to a serious project crisis, but

the situation was turned around into a success. The list of critical success

factors employed was found to be helpful in explaining both the initial failure

and eventual success of the implementation.

23

The list of critical success factors contains the top 10 of critical success

factors articled by Toni Somers and Klara Nelson. The explanatory power of

this list was tested in the case.

The list includes the following factors:

1. Top management support.

2. Project management competence.

3. Interdepartmental co-operation.

4. Clear goals and objectives.

5. Project management.

6. Interdepartmental communication.

7. Management of expectations.

8. Project champion.

9. Vendor support.

10. Careful package selection.

The results in this study revealed that:

• The list of critical success factors, as observed by Nelson and Somer

(top ten of their list), can explain adequately the key issues, which

affect the successful running of ERP project.

• The critical success factors are related to each other in the way that

they affect each other in the same direction. i.e. changes in any one

of them may influence most of the others as well.

• The interdepartmental communication was found to be the most

critical factor for project progress.

• Top management, project management, project champion and

selection of vendor represent the root of the most critical factor

(interdepartmental communication).

3.3.5 (Umble et al. 2003)

24

They identified critical success factors, software selection, and critical

implementation procedures for successful implementation of ERP systems.

The authors started with giving a proper background of ERP systems, and

then they discussed the reasons behind the evolution towards ERP systems.

Afterwards the promises and pitfalls of ERP were sufficiently explained.

Finally, a list of critical success factors for successful ERP implementation

was launched.

Based on reviewing the previous research material in the field of CSFs for

ERP systems, the authors identified the following critical success factors for

ERP implementation:

1. Clear understanding of strategic goals.

2. Commitment by top management.

3. Excellent project management.

4. Organizational change management.

5. A great implementation team.

6. Data accuracy.

7. Extensive education and training.

8. Focused performance measures.

9. Multi-site issues.

In this research, the authors include a case study of an international

incorporation (Huck international Inc.), which successfully implemented an

ERP system. The contribution of the key factors to the successful

implementation of ERP system was discussed and presented in this case

study.

3.3.6 (Parr and Shanks, 2000)

Parr and Shanks presented a project phase model (PPM) of ERP

implementation projects. PPM has three major phases (planning, project and

enhancement).

25

Two case studies of ERP implementation within the same organization, the

first unsuccessful and the second successful, were introduced and analyzed to

identify the necessary critical success factors within each phase of the PPM.

The critical success factors were selected from the former related research

paper. The PPM model was used to understand the ERP implementation

project and to figure out the difference between the two cases.

The PPM with the related CSFs represent guidance for the practitioners

before planning the ERP project by providing a template, which suggests

important CSFs to consider during particular project phases.

The results revealed that the practitioners must pay careful attention to the

planning phase and to the CSFs across the phases of the implementation

project.

3.4 Definition of factors influence the ERP implementation In this section, the factors, which were heavily discussed and included in

earlier related-research papers, are presented and defined.

1. Top management sponsorship:

The ERP project is not the theme of changing the software systems. It is a

matter of restructuring the company and converting the business practices in

addition to its significant contribution to the company’s competitive

advantage. As known, ERP is a resource and time-consuming project (as a

data warehouse project). Therefore it needs to be approved from the top

management to allocate valuable resources (needed people, adequate amount

of time and enough finance) to get the job done.

This factor has been discussed by most of the prior research studies (Nah et al.

2001), (Mabert et al. 2001), (Bingi et al. 1999), (Akkermans and Helden,

2002), (Umble et al, 2003), and (Parr and Shanks, 2000).

26

Top management sponsorship has attracted the attention of practitioners in the

field of data warehouse success factors. As is known, any sizable project (such

as the data warehouse project) needs to be accepted by the top management to

secure the required resources.

2. Presence of Champion:

The success of an ERP project is linked to the existence of a champion who

plays integral roles in leadership, facilitation and marketing the benefits of the

new system to the employees. Usually, this person is supposed to be at senior

management level, so he has the power to make substantial organizational

changes.

This factor was included in many previous studies (Parr and Shanks, 2000),

(Akkermans and Helden, 2002) and (Nah et al. 2001).

The literature about data warehousing has discussed largely the significant

contribution of the existence of champion factor as a critical component

affecting the successful proceeding of data warehouse project.

3. Employee morale:

Employees working on an ERP installation project may face stress and tension

due to long daily shifts and work seven days a week. This may decrease the

employees’ moral rapidly. Top management and project management should

work together to adopt preventive procedures and boost the morale of team

participants. Taking the employees on field trips and arranging parties after

certain achievement of the project, for example after finishing 40% of the

project, are some strategies to boost the employee morale. This factor was

discussed by (Bingi et al. 1999).

A data warehouse is a huge and critical project, which lasts for several months

to two years. The employees may face stress and tension during the

27

implementation phase. Therefore, the high-level management must think

carefully about this challenge and try to reduce it to guarantee a successful

execution of data warehouse initiatives.

4. Interdepartmental cooperation and communication:

An ERP system is actually about tightly integrating different business

functions, so the close co-operation and communication across disparate

business functions would be a natural prerequisite in an ERP implementation

project. Some authors have described the co-ordination and communication

between departments as the oil that keeps everything working properly in

these contexts. (Akkermans and Helden, 2002) and (Nah et al. 2001).

The cooperation between the departments in an organization has a large effect

on the smooth flow of the required information and expertise among the

departments, which strongly influences the successful adoption of data

warehouse technology.

5. Vendor selection:

An ERP project is a mass undertaking which needs sufficient planning and

preparation to complete. Companies do not have enough technical and

transformational skills in-house to manage this project. So it is extremely

important to select a suitable vendor based on some metrics, such as the

vendor’s market focus (small-, mid-, or large-sized enterprises), global rollout

of ERP systems (ability to work in different countries), and substantial

presence of the vendor in many countries. This discussion was highlighted by

(Akkermans and Helden, 2002), and (Bingi et al. 1999).

In the case of data warehouses, the expensive and the risky nature of data

warehouses have forced the potential adopters to pay extra attention in

selecting appropriate vendors to increase the possibility of having successful

data warehouse initiatives.

28

6. Great and authorized implementation team:

This is one of the most important factors, which effect the ERP

implementation. Building a cross-functional and great team is a critical

prerequisite based on selecting people for their skills, past accomplishments,

reputation and flexibility, as indicated by (Nah et al, 2001), (Umblel et al,

2003), (Parr and Shanks, 2000), (Bingi et al, 1999), (Akkermans and Helden,

2002), and (Mabert et al. 2001).

The team should have a mix of external consultants and internal staff, and

they should to be assigned to the project in full-time work basis.

Compensations and incentives should be provided to the team for successfully

implementing the system on time and within the allocated budget.

The members must be empowered to make critical and rapid decisions.

Data warehouse professionals heavily stress having a good cross-functional

team when the subject of possessing a successful data warehouse appears on

the surface of the discussion table.

7. Accurate definition of project’s objectives and goals:

It is crucial to start the IT project with a clear definition of goals and the way

to accomplish them. (Mabert et al, 2001), (Parr and Shanks, 2000), (Umble et

al, 2003), (Akkermans and Helden, 2002), and (Nah et al. 2001).

It is important, as well, to define the expectations and the deliverables from

this project. In the case of an ERP project, the organization must know the

following: why the ERP system is being selected and implemented? What

critical business needs the system will address? and finally, how to achieve

these goals in the most efficient and effective manner?

In the case of a data warehouse project, it is crucial to define apparently, from

the very early stages of the project, the objectives and what is expected from

the data warehouse technology, then try to match the expectations with the

29

real achievements to start the project in the right direction before the new

system comes to life, taking into consideration that the apparent definition of

objectives assists to build relevant guidelines for project implementation.

8. Existence of consultants:

Because the ERP market has grown so fast, there has been a lack of competent

consultants. It is important and challenging to find the right consultants and

keep them throughout and after the implementation phase. The enterprise

must establish a knowledge transfer process from outside consultant to in-

house staff for both system configuration information and long-run

maintenance. One technique could be by involving the in-house staff in all the

implementation phases of the ERP system together with the external

consultants and building training courses.

(Parr and Shanks, 2000), (Mabert et al, 2001), and (Bingi et al. 1999) have

investigated this factor in their research works.

Building a successful data warehouse demands qualified consultants to

provide the adopter with necessary insights into constructing the system, in

addition to educating the users to interact effectively and efficiently with the

new system.

9. Implementation time:

It is necessary for an ERP project to set milestones and an end date, as stated

by (Parr and Shanks, 2000), and (Bingi et al. 1999). Since ERP systems are

modular-based systems it is possible for companies to phase-in one module at

a time. The length of the implementation is effected by the number of modules

to be installed, the scope of implementation (number of units in the

organization), the degree of customization (customize the ERP system based

on the specific requirements of the enterprise), and the number of interfaces

with other applications.

30

To make sure that the data warehouse project is not behind the predetermined

schedule, it is necessary to design a fixed schedule and define the end date for

each phase in the implementation process.

10. Focused performance measures:

Performance measures must be constructed to measure the achievements

against project goals and to encourage the desired behavior by all functions

and individuals. Such measures can encompass on-time deliveries, gross profit

margin, customer order-to-ship time, inventory turns, and vendor

performance.

Project performance measures must be included from the beginning of the

project. Additionally, if system implementation is not tied to compensation,

the project will not be successful. For example, if the team members will get

their bonuses next year even if the system is not yet implemented, the

successful implementation is less likely.

This factor was argued by (Umble et al, 2003), (Nah et al. 2001), (Akkermans

and Helden, 2002), and (Mabert et al. 2001).

In data the warehouse case, measuring the achievements from the project

against the goals, throughout the running of the project, is critical for success.

The reason for that is to make sure that the company is on the right track and

to correct the unnecessary activities.

11. Business process reengineering (BPR) and minimum customization:

Installing an ERP system includes reengineering the existing business

processes to the best business process standard followed in the industry. All

the business processes must agree to the ERP model. Organizations should be

willing to change the business to fit the software with minimal customization.

Modifications should be avoided or reduced to reduce errors and to take

advantage of newer versions of the ERP systems. It is not easy to get every

one to agree to the same process. Sometimes business processes are so unique

31

and valuable that the company wants to preserve them. In this case the

company has two options; Change its business processes to conform to the

ERP package or customize the ERP package to suit the company’s needs. The

2nd option is costly because the costs of customization, future maintenance and

upgrade will greatly increase.

(Parr and Shanks, 2000), (Bingi et al. 1999), and (Nah et al. 2001) discussed

this factor in the context of ERP implementation.

BPR seems to be an ERP-specific factor since an ERP system adopts the so-

called business best practices known in the industry. This stimulates and

encourages the adopters to change their way of doing business and adopt the

ERP system way. In the case of a data warehouse project, there is no need to

change the existing business process. Data warehouse technology comes up

with a new way of analyzing and processing the business transactions which

mostly did not exist before the installation of data warehouse applications,

such as a multidimensional view of analysis.

12. Integration:

Many companies feel that having a single application from a single vendor

seems to serve the customer more efficiently and makes it easier to maintain

and upgrade the system. Unfortunately no single application can meet all the

company’s requirements and needs. Some companies may use other

specialized software to meet their needs and requirements. An ERP system

needs to be integrated with all of that software. The ERP system will serve as

a backbone of the company’s IS. Other software is bolted on to the ERP

system. In other words ERP systems are installed on the top of the disparate

legacy applications to integrate them and make them work together in a

unified manner. There is third party software called middleware which can be

used to integrate different specialized software with the ERP system.

Unfortunately middleware is not available for all software that is available on

32

the market. Middleware vendors concentrate only on the most popular

software package in the market.

This factor was researched by (Bingi et al. 1999), and (Nah et al. 2001).

This factor is important to secure the smooth running of data warehouse

applications. The data warehouse system works as a big data store collecting

the data from different transaction systems and putting them all in one place.

Therefore the data warehouse must be integrated with those application

systems.

13. Careful selection of packaged-software.

Selecting an adequate software package is an important step in the ERP

implementation process, as shown by (Nah et al. 2001), and (Akkermans and

Helden, 2002).

Some packages are more suited for larger firms and others are more suited for

smaller ones. Some packages have become a de facto industry and others have

stronger presence in certain places in the world. Once the selection of the

package has been done, the next step would be the decision of what versions

or modules would be the best to fit the organization’s needs. These decisions

have to be made in the very early stage of the ERP implementation project. If

the wrong choices are made then the company faces a big problem that can

only be solved by doing time consuming, costly and high risk modifications

on the selected software package.

Selecting adequate packaged-software contributes largely to the success of

data warehouse technology. The software selection is done based on certain

criteria, which are identified after defining and analyzing the companies’

situation and requirements, such as type of industry, size of company and

others.

14. Data accuracy:

33

ERP systems require data accuracy. Because of the integrated nature of the

ERP system, if someone enters the wrong data, the mistake can affect all the

functional areas in the enterprise. Educating the users about the importance of

the data accuracy should be a top priority of the ERP implementation process.

Data accuracy was discussed by (Umble et al. 2003).

As cited earlier, data warehouse technology serves as a huge data repository,

which collects the data from different data sources. This data store is used by

many end-users in the company for different purposes. Therefore, a careful

consideration must be paid on the quality of the raw data stored in a data

warehouse. The main reason is that it will affect massively the strategic

position of the company by influencing the decision making initiatives.

15. Extensive education and Training

Training and updating the employees’ knowledge of ERP is a major

challenge. ERP implementation requires a huge mass of knowledge to enable

people to use, cope and solve problems within the framework of the system.

Training employees to use ERP is not as simple as training them in any other

packaged-software such as a Microsoft package. An ERP system is extremely

complex and demands intensive training; it is difficult for the trainers to pass

the knowledge to the users within a short period of time. Top management

should understand this aspect and should be willing to spend adequate money

on educating and training the end users.

This factor was explored by the (Bingi et al. 1999), (Umble et al. 2003), and

(Mabert et al. 2001).

Training and education of the employees are required in a successful data

warehouse project. A data warehouse is not a simple project or an easy-to-

learn system. It demands time to educate and transfer the knowledge to users

by setting up training courses and distributing related-material.

34

3.5 Summary of the chapter ERP stands for Enterprise Resource Planning and is a computer-based system

that integrates all components of the business, including planning,

manufacturing, sales, and marketing. This chapter introduced the reader to the

ERP from different aspects starting with the definition, going through the

common characteristics and features and ending with the critical success

issues for ERP implementation.

ERP has been intensively discussed in the literature and empirical studies,

particularly the CSFs aspect. On the other hand, there is an obvious lack of

discussion concerning the CSFs in data warehouse literature and empirical

studies. Therefore I found that it is relevant to define these factors and

measure their influence on data warehouse technology, since both systems are

expensive, complex and risky undertakings. The follower can find many

similar critical factors have been discussed by the professionals in both areas

of expertise.

35

4. Critical success factors of data warehouse implementation

4.1 Objective and Structure

Chapter 4 is considered the main theoretical chapter in the thesis. It serves the

study more from the background information point of view regarding the

CSFs influencing the adoption of data warehouse technology.

Section 4.2 presents the previous relevant research papers, empirical and

theoretical, which investigated the CSFs in the data warehouse adoption

project. Then section 4.3 defines and categorizes these factors into respective

dimensions. In section 4.4 the phases of data warehouse project are defined

and discussed, and then the critical factors are classified and assigned into

these phases. Finally, section 4.5 identifies the scope of this study, by defining

the factors that will be investigated throughout the remaining parts of the

thesis.

This chapter was structured and designed based on reviewing the following

research papers: (Joshi and Curtis, 1999), (Wixom and Watson, 2001),

(Hwang et al. 2004), (Mukherjee and D’Souza, 2003), (Solomon, 2005),

(Hurley and Harris, 1997), and (Watson et al. 2002).

4.2 Prior relevant studies and research papers The difficulty and failure implementation of data warehouse technology were

discussed in the literature. But the research (empirical and theoretical) on

critical success factors influencing data warehouse implementation is

infrequent and fragmented.

Unfortunately the majority of the available research focused largely on

technological and educational aspects, which represent the operational level in

the organization.

36

Earlier studies in data warehousing discussed partly and slightly

organizational, environmental and project-related dimensions, by investigating

a single or a couple of factors under one or more dimensions. This obviously

has led to lack of exploring the impact of these dimensions, which represent

the managerial and strategic levels in the organization.

This study is timely and important because it sheds light on organizational,

environmental, and project-related dimensions, together as a package, which

influence the adoption of data warehouse technology in general, and in

Finnish companies in particular.

For master thesis purposes, the most relevant and important factors under the

umbrella of the selected dimensions will be investigated. The selection of

factors was done based on reviewing the relevant prior research papers in the

field of data warehousing and ERP systems.

The table below aims to provide a list of preceding related-studies in the field

of data warehousing, and then presents the factors, discussed in each study,

and a short overview of each study.

Authors Factors About the Paper

Joshi and

Curtis

Technical issues (data warehouse

architecture and access tools),

training factors, data related

factors and clear identification of

objectives and organizational

needs.

It is a theoretical study in which the

Authors stated some

recommendations for successful

implementation of data warehouse.

Wixom and

Watson

Organizational factors

(management support and

Champion), Project factors (User

participation, resources and team

skills) and Technical factors

An empirical study which

investigates the model of data

warehousing success through cross-

sectional mail survey to data

warehousing managers and data

37

(source systems and development

tools)

suppliers from 111 organizations in

U.S.

Hwang et

al.

Organizational dimension

(organization’s size, champion,

Top management support and

internal needs), Environmental

dimension (Business competition

and selection of vendors) and

Project-planning dimension

(project team’s skills,

Coordination of organizational

resources, consultants support and

end user support)

An empirical study conducted to

investigate the factors influencing the

adoption of data warehouse

technology in the banking industry in

Taiwan. The data was gathered based

on the prior-related research and a

mailed questionnaire to CIOs in 50

domestic banks in Taiwan.

Mukherjee

and

D’Souza

Technical issues (data,

technology and expertise),

Management issues (executive

sponsorship and operating

sponsorship), Goals and

Objectives issues ( business need

and clear link to business

objectives), Users issues (user

involvement, user support and

user expectation), Organizational

issues (organizational resistance

and organizational politics) and

System issues (evolution and

growth)

A theoretical study presents a

framework to understand the critical

success factors of the data warehouse

in each phase of the data warehouse

implementation process.

38

Solomon Identifying the project’s scope,

source system identification, data

quality planning, Technical

matters (Data model design, ETL

tools, Relational database

software selection, data transport

and conversion tools), and end-

user support

The Author in this theoretical study

provided useful guidelines to avoid

expected obstacles in enterprise-sized

data warehouse projects and increase

the likelihood of success based on the

prior-related research and his

experience in this field.

Hurley and

Harris

Team skills, Technical

infrastructure, Project

management, Good vendor,

Business imperative, Clear

objectives, and data quality.

An empirical study discussed a

survey conducted among the pacific

countries (Australia, New Zealand

and Singapore) across the industrial

companies to have a thorough

understanding of the data warehouse

issues through investigating different

aspects such as Management issues,

technical matters, reasons for data

warehouse approach, reasons for data

warehouse success and reasons for

data warehouse failure.

Watson et

al.

Business need, Champion, Top

management support, user

involvement, training matters,

Technical issues (adequate tools)

Accurate definition of the

project’s objectives, growth and

An empirical study geared to answer

the following question: why some

organizations are receiving more

significant returns than others after

the data warehouse implementation?

Three case studies of data warehouse

39

upgradeability, Organizational

politics, skilful team.

initiatives from diverse industries

were introduced to answer the above-

mentioned question.

Table 4.1

4.2.1 (Joshi and Curtis, 1999)

Joshi and Curtis explored some key issues that any organization should think

about before planning to adapt data warehouse technology.

Based on reviewing the related research papers, they developed important

issues that the organization must consider to have a successful planning of a

data warehouse project. The following list is a summary of their work:

1. Data warehouse development issues

• Alignment of data warehouse project to business needs

• Define clearly the Scope of the data warehouse project

2. Data warehouse architecture issues

• Defining the appropriate database architecture and adequate

development and analytical tools such as selection of DBMS, Online

analytical processing, and data warehouse development options.

3. Data issues

• Careful consideration of the span and extend of the data

• Defining adequate metadata and appropriate tools to maintain it

• Identification of useful external and qualitative data sources

• Careful consideration of the data loading tools

• Managing and increase the quality of the data integrity

4. User Access issues

• Provide the broadest range of possible user access, interface and

analysis tools

• Adequate training courses to prepare the users for the tools.

4.2.2 (Wixom and Watson, 2001)

40

They held an empirical investigation of the factors influencing data warehouse

success among the American organizations.

A cross-sectional survey was used in this study to build up a model of data

warehousing success. This questionnaire was distributed among data

warehouse managers and data suppliers from 111 organizations, to gain

relevant data about implementation and success factors of data warehouse.

They cited, in their studies, seven factors considered to be crucial in the

adoption of data warehouse based on reviewing the prior related research

materials (Management support, Champion, Resources, User participation,

Team skills, Source Systems, and Development technology).

The results revealed that the following factors have a big and positive

influence on the successful adoption of data warehouse project; Management

support, Resources, User participation, Team skills, Quality source systems,

and Better development technology.

4.2.3 (Hwang et al. 2004)

The researchers intended to explore the critical factors affecting the adoption

of data warehouse technology in the banking industry in Taiwan.

There focus scope was on the following packaged-dimensions

(Organizational, Environmental, and Project dimensions). A questionnaire

survey was designed and used to achieve the study’s objective. A total of 50

questionnaires were mailed to CIOs in local banks. After an intensive review

of prior relevant studies, a total of ten factors influencing the success of data

warehouse project were developed (Size of bank, Champion, Top

management support, Internal needs, Degree of business competition,

Selection of vendors, Skills of project team, organization resources, User

participation, and Assistance of information consultants).

After collecting the results from the questionnaire, they found that top

management support, size of the bank, effect of champion, internal needs and

41

competitive pressure would affect the adoption of data warehouse technology

in banking industry in Taiwan.

4.2.4 (Mukherjee and D’Souza, 2003)

Mukherjee and D’Souza presented a framework which might help the data

warehouse people to visualize how critical success factors can be included in

each phase of data warehouse implementation process.

They found that the data warehouse implementation process follows the three-

phased pattern of evolution (Pre-implementation, Implementation and Pos-

Implementation phases).

After reviewing previous related-studies, a list of 13 critical implementation

factors was developed; Data, Technology, Expertise, Executive sponsorship,

Operating sponsorship, Having a business need, Clear link to business

objectives, User involvement, User support, User expectation, organizational

resistance, organizational politics, and Evolution and growth.

They have discussed each factor and the contribution of each factor in every

phase of data warehouse implementation process.

4.2.5 (Solomon, 2005)

Solomon provided guidelines to help managers avoid common pitfalls and

obstacles in enterprise-level data warehouse projects based on reviewing

previous related-studies and extensive field experience.

The following are the guidelines that must be considered, by the

organizations, to increase the chances for success

• Service level agreements and data refresh requirements.

• Source system identification

• Data quality planning

• Data model design

• Extract, transform, and load tool selection

42

• Relational database software and platform selection

• Data transport

• Reconciliation process

• Purge and archive planning

• End-user support

4.2.6 (Hurley and Harris, 1997)

Hurley and Harris described a survey conducted by KPMG management

consulting and the Nolan Norton institute. This survey was distributed among

the Pacific’s senior information managers in mid- and large-sized companies.

The survey aimed to achieve a coherent understanding regarding data

warehousing initiatives.

The findings from the survey revealed that data warehouse technology heavily

increases financial and business returns in the adopters. They found also the

following factors for successful data warehousing initiatives: project teal

skills, Technical infrastructure, Project team, Technical architecture, Good

vendor capability, Business imperative, clear objectives, Data quality, and IS

alignment.

4.2.7 (Watson et al. 2002)

The researchers presented an explanation of why some organizations realize

more exceptional benefits than others after data warehouse installation.

The authors started by giving a basic background about a data warehouse.

Then they went through the obtainable benefits gained from data warehouse

installation in general by the adopters.

Three case studies of data warehousing initiatives, a large manufacturing

company, an internal revenue service and a financial services company, were

discussed within the context of the suggested framework.

43

The results from the case studies highlighted the benefits achieved by the

three organizations. The researchers noticed that some of them considered

more significant payoffs than the other adopters.

The researchers built an argument about the main issues behind the success in

the three cases. The argument led to the following critical success factors:

Business need, Champion, Top management support, user involvement,

training matters, Technical issues (adequate tools), Accurate definition of the

project’s objectives, growth and upgradeability, Organizational politics,

skilful team.

4.3 Definition of factors influencing the data warehouse

implementation The findings from earlier related-materials (either theoretical or empirical

ones) have flagged the following critical success dimensions that have to be

taken into account by global managers:

1. Organizational factors.

2. Environmental factors.

3. Project factors.

4. Technical factors.

5. Educational factors.

The first four dimensions were derived directly from earlier related- studies. I came

up with the last dimension to include the factors that mainly discuss the educational

and learning matters.

4.3.1 Organizational factors:

The organizational dimension is an important aspect in the adoption of data

warehouse applications.

By taking into consideration the organizational factors, many of the obstacles

and barriers faced will be altered.

The following factors are included under the organizational dimension:

44

1. Size of the organization:

Size of the organization greatly affects the adoption of data warehouse

technology. The larger organization has more resources and capital to be

assigned for a data warehouse project. Large organizations mostly have

enough resources and power to overcome obstacles, such as huge set-up costs

and labor expenses, in data warehouse project. This factor has been

investigated by (Hwang et al. 2004).

2. Existence of champions:

Champions are the people from inside the organization, who appreciate and

support the adoption of new technology.

Existence of champions has a critical impact on the embracing of data

warehouse technology. They play an integral role in providing necessary

information, required resources, needed assistance, political support and

stimulate the staffs to adapt and cope with the new technology, as discussed

(Wixom and Watson, 2001), (Hwang et al. 2004), (Watson et al. 2002).

3. Top management sponsorship (executive and operating):

The commitment of top management support is very important to pass over

sudden barriers and complexities in a data warehouse project, as highlighted

by (Wixom and Watson, 2001), (Hwang et al. 2004), (Watson et al. 2002),

and (Mukherjee and D’Souza, 2003). With the top management support the

organization can secure required capital, human support, and availability and

coordination of other related internal resources in adoption and development

process.

4. Business Internal needs:

The alignment of the data warehouse to business needs is a crucial step in a

data warehouse adoption project, as cited by (Hwang et al. 2004), (Mukherjee

and D’Souza, 2003), (Joshi and Curtis, 1999), and (Watson et al. 2002).

Before commencing such a gigantic effort, it is important to elucidate the

45

strategic business objectives and needs that a data warehouse would be

expected to meet. A data warehouse is expected mainly to meet the need of

having a unified data repository, which encompasses integrated information to

support the initiatives of different business units.

5. Organizational resistance:

Employee resistance is the emotional factor exhibited as a result of

organizational change. This resistance basically is driven by the fear of

loosing their jobs, by replacing labor-intensive production with automated

production or replacing technology-incompetent employees with technology-

savvy ones after implementing the new technology. Consequently, it is

important to understand the employee resistance and try to reduce it. The

resistance must be addressed appropriately by encouraging the staff to accept

and adapt the new technology through training courses and lectures.

Mukherjee and D’Souza (2003) pointed out the significance of this factor to

secure a comprehensive adoption of data warehouse technology among the

users.

6. Organizational politics:

Organizational legislation and regulations are developed to govern and control

processes and activities in the enterprise and achieve the long-term goals and

objectives. The organizational policy provides specific policy (detailed)

information of how the legislation serves to achieve the long-term objectives

in the organization. The policy is usually accompanied by procedural

information, explaining the specific steps involved in executing the process in

question.

In the matter of data warehouses, it is important to secure the alignment of

data warehouse technology to the legislation in order to achieve the long-run

objectives. The policies provide detailed information about how the alignment

46

(between data warehouse and legislation) can be established to achieve the

long-term objectives.

(Mukherjee and D’Souza, 2003), and (Watson et al. 2002) introduced this

factor as a key issue in a successful data warehouse.

4.3.2 Environmental factors:

The enterprise incorporates in a dynamic environment with high possibilities

of sudden and uncontrolled changes. The enterprise must measure and reduce

the uncertainties in the surrounding environment and create competitive

advantages by adopting newer information technology. Below is the list of

factors under the environmental dimension.

1. Business competition:

Enterprises often try to boost their competitive advantage by adopting new

information technology. Previous researchers, such as (Hwang et al. 2004),

have shown that business competition is directly allied with the adoption of

new information technology. The organization is no longer to maintain the

piloting edge in its industry without the adoption of a data warehouse if the

competitors are adopting or have adopted this technology.

2. Selection of vendors:

Selection of vendors largely affects the decision of data warehouse adoption,

as shown by (Hwang et al. 2004), and (Hurley and Harris, 1997). Today’s

organizations intend to outsource their business applications. In this regard

companies must be aware while selecting the vendors. Data warehouse

technology itself is not only a software package. It is a time-consuming and

very expensive project, and the plans suggested by vendors may not be

completely convenient for an enterprise itself. Therefore, the enterprise cannot

leave all execution plans and operating details in the vendor’s hands.

47

3. Compatibility with industry standards and governmental regulations:

There are regulations and industry standards, which regulate and govern the

transactions, communications and processes, in the business field. These

regulations and standards are established by authorized parties such as

government or business standard setters. Companies must understand and

adapt these standards and regulations by getting their systems aligned with

them. Example, if the regulation allows a partner, in Supply chain, to view

certain types of information, then the data warehouse should restrict the

partner’s authority to view this type of information.

4. Compatibility with partners:

A company is no longer to be a star performer in its industry without having

tight relationships with direct, upstream and downstream, partners. This tight

relationship is driven by the compatibility with direct partner’s systems. When

enterprises intend to install a new system (like data warehouse), they must

understand the systems adopted by direct partners and try to figure out a

suitable new system. This procedure is considered a plus point to maintain the

relationships with partners and heighten the overall performance of the supply

chain.

A data warehouse is a data source which stores a huge amount of relevant data

and can be used by direct partners to collect needed, accurate and real-time,

data for supply chain matters. As a result, the compatibility with direct

partners’ systems is important to facilitate successful interaction between the

systems of direct partners and the focal company when exchanging the data.

4.3.3 Project-related factors:

The project-related dimension is one of the most important dimensions in

adoption of data warehouse technology. Project-related factors are related to

project plan, analysis, development and control.

48

The following factors were discussed in the context of the project-related

dimension.

1. Skills of project team:

The skills of project team factor has an endless impact on the success of a data

warehouse project. The members must be proficient in data warehousing

matters. Possessing strong background and knowledge of new technology

adoption, coupled with better communication capability positively influences

data warehouse implementation. It is necessary to select the members from

different departments, to add diverse values to data warehouse project, as well

as educate them in different aspects, as shown by (Wixom and Watson, 2001),

(Hwang et al. 2004), (Watson et al. 2002), and (Hurley and Harris, 1997).

2. Emergence and Coordination of organizational resources:

Resources comprise money, people, and time, which are necessary to

successfully finish the project. Resources are so important in data warehouse

projects, because data warehouses are high-priced, time-consuming and

recourse-intensive initiatives. Coordination and correct allocation of resources

can help project teams to meet their project milestones and overcome

organizational obstacles. Coordination of resources can be accomplished by

affording enough capital, sufficient time and required labor, as indicated by

(Wixom and Watson, 2001), and (Hwang et al. 2004).

3. End-user involvement:

End-user involvement has a direct influence on successful implementation of

information technology, as mentioned by (Wixom and Watson, 2001),

(Hwang et al. 2004), (Watson et al. 2002), (Mukherjee and D’Souza, 2003)

and (Solomon, 2005). Better user participation increases the probability of

managing user’s expectations and satisfying user requirements. Selection and

inclusion of fitting users in the project team is an important mission. Adequate

training can help users to explore the desirable information positively and in a

49

much more effective mode. Sufficient user involvement reduces the resistance

from end users to use newer information technology.

4. Support from outside consultants and expertise:

As known, data warehouse technology is a time-consuming and expensive

project with high risk possibilities. Consultants who possess much experience

positively influence the success and smooth adoption of new technology. The

consultants can be employed to provide ideas and lend a hand to organizations

that lack the experience to adopt, install and maintain new information

technology, as cited by (Hwang et al. 2004), and (Mukherjee and D’Souza,

2003).

5. Accurate definition of project’s priorities, scope and goals:

Building a data warehouse symbolizes a massive investment of resources and

effort. So it is necessary to define clearly the scope, goals and priorities of the

overall project before any step to be undertaken. Inaccurate definition of the

project’s priority may cause bottlenecks and shortage in project resources

resulting in delays in the project’s schedule and processes, as indicated by

(Watson et al. 2002), (Hurley and Harris, 1997), (Solomon, 2005), and

(Mukherjee and D’Souza, 2003).

4.3.4 Technical factors:

The technical dimension was measured by discovering technical problems that

appeared and technical limitations that occurred during the implementation of

data warehouse technology.

The discussion below is regarding the sub-factors under the technical

dimension.

1. User interface:

Extra-care must be taken to select suitable tools that will be interfaced with

the end-user, as stated by (Solomon, 2005), and (Joshi and Curtis, 1999). The

50

project team should work hard on weighing up the friendliness and easiness of

the user interface. The user interface must guarantee to provide the users with

the greatest flexibility in the choice of access methods and strategies.

Friendliness, easiness and flexibility of user interface tools lead to reduce the

resistance from end users to new information technology and increase the

adaptability.

2. Technical resources availability :

Technical resources are hardware, software, methods and programs used in

carrying out a project. A good visualization of technical resources allows

managers to conceptualize future states and recognize benefits more

realistically, as shown by (Joshi and Curtis, 1999), (Wixom and Watson,

2001), (Solomon, 2005), (Hurley and Harris, 1997), and (Watson et al. 2002).

These resources influence effectiveness and efficiency of the development

team to actualize the needs and requirements of the organization. This factor

is the most talked about factor among critical success factors of data

warehouse technology in the prior related studies.

3. Quality of data sources:

Data sources and their governance policies should be identified clearly,

especially in large data warehouse initiatives, where the data is extracted from

many data sources. The quality of organization’s present data is another

important aspect, which affects the systems initiatives. Data in a data

warehouse often comes from diverse and heterogeneous sources. So the need

for data standards can result in easier data handling, fewer problems and

eventually a more successful system, as thrashed out by (Wixom and Watson,

2001), (Solomon, 2005), and (Hurley and Harris, 1997).

4.3.5 Educational factors:

This dimension answers the following question:

51

How dose the organization assure a comfortable interaction between users and

new technology, which concretely leads to reduce users resistance and widen

users acceptance of new technology?

The following is the answer of the above question.

1. Training courses:

The end users must be continuously informed and aware of the latest

developments regarding data warehouse technologies. Increasing users’

knowledge can be done by setting-up training courses and distributing related-

materials, such as books and research papers. Adequate training assists the

users in understanding the newer technology and reduces their resistance, as

pointed out by (Joshi and Curtis, 1999), (Solomon, 2005), and (Watson et al.

2002).

2. Certified trainers :

The trainers contribute positively to increasing the success of new technology

and reducing the users resistance (Joshi and Curtis, 1999), (Solomon, 2005),

and (Watson et al. 2002). Certified trainers are employed to blur the lines

between non-technology-knowledge users and technology-knowledge users.

One technique could be involving the in-house users in all implementation

phases of the data warehouse system together with the trainers to transfer the

knowledge to users, in addition to setting training lectures and distributing

related-materials.

3. Availability of best practices adaptors:

The availability of good examples, regarding successful implementation of

data warehouses, supports the decision of adapting the data warehouse and

facilitating the implementation process. Best practices adopters represent the

source, where an organization can have feedback to successfully implement

new information technology and overcome obstacles faced by best practices

adopters.

52

4.4 Classifying the CSF based on the phased logic of the data

warehouse implementation Prior research papers in the field of ERP systems identified different phases in

the ERP life cycle. (Nah et al. 2001) classified the key factors of ERP systems

into respective phases according to Markus and Tanis’s ERP life cycle model,

which includes four phases (Chartering, Project, Onward, and Upward).

(Parr and Shanks, 2000) introduced three major phases in ERP

implementation projects, which are (Planning, Project and enhancement).

In case of data warehouses, a few earlier research papers have identified a

sorting of data warehouse project life cycle.

After an intensive review of former research papers, the three-phased pattern

of data warehouse evolution, proposed by (Mukherjee and D’Souza, 2003),

was found and adapted. This sorting includes three phases; Pre-

implementation, Implementation and Post-implementation.

The first phase encompasses a bunch of activities and tasks carried out before

the actual deployment of data warehouse technology.

The second phase includes a group of activities and tasks that arise during the

actual installation of data warehouse technology.

The third phase includes a group of activities and tasks that happen after the

actual installation of the data warehouse technology.

The scope of the following part is to answer this question: what are the factors

influencing data warehouse technology in each phase of above-mentioned

ones?

4.4.1 Pre-implementation phase

53

There are many tasks and activities occurring in this phase, such as needs

analysis, capability assessment, problem exploration and identification, and

development of goals.

In this phase, the critical factors support the project by ways of securing

needed resources, problem identifications, goals clarification, understanding

informational needs and securing smooth progression of data warehouse

project.

The following factors are believed to support the adoption of a data warehouse

in this phase:

Organizational factors:

1. Size of the organization

2. The existence of champions

3. Top management sponsorship (executive and operating)

4. Business Internal needs

5. Organizational politics

6. Organizational resistance

Environmental factors:

1. Business competition

2. Selection of vendors

3. Compatibility with industry standards and governmental regulations

4. Compatibility with partners

Project factors:

1. Emergence and Coordination of organizational resources

2. Accurate definition of project’s priorities, scope and goals

3. End-user involvement

Technical factors:

1. Technical resources availability

Educational factors:

1. Availability of Best practices adaptors

54

4.4.2 Implementation phase

In this phase, analysis, design and development of the technical backbone of

the data warehouse technology are undertaken. Also an implementation plan

is developed, resources are assembled, and the installation processes of data

warehouse technology are undertaken and addressed in place.

This phase is often the most time-consuming and resource-spending phase in

data warehouse development.

The critical factors in this phase assure flexible and successful ongoing of the

data warehouse project.

The following factors are supposed to influence the adoption of the data

warehouse in this phase:

Organizational factors:

1. Size of the organization

2. The existence of champions

3. Top management sponsorship (executive and operating)

4. Organizational politics

5. Organizational resistance

Environmental factors:

1. Business competition

2. Selection of vendors

3. Compatibility with industry standards and governmental regulations

4. Compatibility with partners

Project factors:

1. Emergence and Coordination of organizational resources

2. Skills of project team

3. End-user involvement

4. Support from information consultants and expertise

5. Accurate definition of project’s priorities, scope and goals

Technical factors:

1. Technical resources availability

55

2. User interface

3. Quality of data sources

Educational factors:

1. Availability of Best practices adaptors

2. Training courses

4.5.1 Post-implementation phase Factors

In this phase, data warehouse technology is assessed to determine weather the

project objectives are met or not. Data warehouse implementation may take

two or more years. Therefore during that period the organization may

experience many changes, which might influence data warehouse adoption

badly or well. Accordingly the organization must decide weather to end the

implementation phase and accept the data warehouse as it is or to go back

some steps and upgrade the system.

The main activities, in this phase, are collecting the feedback about data

warehouse technology, upgrading of data warehouse applications and

maintaining system stability (smoothing the ongoing of the data warehouse

system without any interruption and facilitating the effective interaction

between the staff and the system).

The following Factors are believed to affect the successful adoption of data

warehousing in this phase:

Organizational factors:

5. size of the organization

6. The existence of champions

7. Top management sponsorship (executive and operating)

8. Organizational resistance

Project factors:

1. Skills of project team

2. End-user involvement

3. Support from information consultants and expertise

56

Technical factors:

1. Quality of data sources

Educational factors:

1. Availability of Best practices adaptors

2. Training courses

3. Certified trainers

The diagram below illustrates the phases of data warehouse implementation

process and critical success factors occurring in each phase. The key words

below the diagram highlight the meaning of the numbers in the diagram.

Figure 4.1

Key words: Organizational factors:

1. Size of the organization

2. The existence of champions

3. Top management sponsorship (executive and operating)

4. Business Internal needs

57

5. Organizational resistance

6. Organizational politics

Environmental factors:

1. Business competition

2. Selection of vendors

3. Compatibility with industry standards and governmental regulations

4. Compatibility with partners

Project factors:

1. Skills of project team

2. Emergence and Coordination of organizational resources

3. End-user involvement

4. Support from information consultants and expertise

5. Accurate definition of project’s priorities, scope and goals

Technical factors:

1. User interface

2. Technical resources availability

3. Quality of data sources

Educational factors:

1. Training courses

2. Certified trainers

3. Availability of Best practices adaptors

The following table summarizes the long discussion, under the phases of data

warehouse project life cycle, by assigning the critical factors into the

respective phases.

Factors Pre-implementation Implementation Post-implementation

Size X X X

Champion X X X

Top management X X X

Internal needs X

Org. Resistance X X X

Org. Politics X X

Business

competition

X X

58

Vendor support X X

Industry standards X X

Partner

compatibility

X X

Project team X X

Org. resources X X X

End-user

involvement

X X

Consultants X X

Clear objectives X X

User interface X X

Technical resources X X

Data source quality X X

Training courses X X

Certified trainers X X

Best practices X X X

Table 4.2

4.5 Factors investigated in the thesis This study provides additional insights to supplement the findings from

foregoing research in the area of critical success factors of Data warehouse

implementation.

Although the organizational, environmental and project-related issues in data

warehousing are of importance, little attention was paid to these aspects. This

study attempts to fill the space and add these aspects to the main subjects,

which need to be discussed, regarding the key factors of data warehouses.

Below is the list of selected dimensions and the factors that will be

investigated throughout the remaining parts of this study.

4.5.1 Organizational factors:

59

1. The existence of champions

2. Top management sponsorship (executive and operating)

3. Business Internal needs

4.5.2 Environmental factors:

1. Business competition

2. Selection of vendors

3. Compatibility with partners

4.5.3 Project-related factors:

1. Skills of project team:

2. Emergence and Coordination of organizational resources:

3. End-user involvement:

4. Support from information consultants and expertise:

These factors are selected based on reviewing former related studies in the

field of critical success factors of data warehouse technology and ERP

systems, as indicated by the following table:

Main Factor Sub-factor Data warehouse

research

ERP research

Organizational

factor

Wixom and

Watson, Hwang et

al., Mukherjee and

D’Souza, and

Watson et al.

Mabert et al., Nah

et al., Bingi et al.,

H Akkermans and

Helden, Umble et

al, and Parr and

Shanks

The existence of

champions

Wixom and

Watson, Hwang et

al., and Watson et

Nah et al., H

Akkermans and

Helden, and Parr

60

al. and Shanks

Top management

sponsorship

Wixom and

Watson, Hwang et

al., Watson et al.,

and Mukherjee and

D’Souza

Mabert et al., Nah

et al., Bingi et al.,

H Akkermans and

Helden, Umble et

al, and Parr and

Shanks

Business internal

needs

Hwang et al.,

Mukherjee and

D’Souza, Joshi and

Curtis, and Watson

et al.

----------------

Environmental

factor

Hwang et al., and

Hurley and Harris

Bingi et al., and H

Akkermans and

Helden

Business

competition

Hwang et al. ----------------

Selection of

vendors

Hwang et al., and

Hurley and Harris.

Bingi et al., and H

Akkermans and

Helden

Compatibility with

partners

----------------- ----------------

Project-related

factors

Wixom and

Watson, Hwang et

al., Mukherjee and

D’Souza,

Solomon, and

Hurley and Harris.

Mabert et al., Nah

et al., Bingi et al.,

H Akkermans and

Helden, Umble et

al, and Parr and

Shanks.

Skills of project Wixom and Mabert et al., Nah

61

team Watson, Watson et

al, Hwang et al.,

and Hurley and

Harris.

et al., Bingi et al.,

H Akkermans and

Helden, Umble et

al, and Parr and

Shanks.

Emergence and

coordination of

organizational

resources

Wixom and

Watson, and

Hwang et al.

Bingi et al., and H

Akkermans and

Helden

End-user

involvement

Wixom and

Watson, Hwang et

al., Watson et al,

Mukherjee and

D’Souza, and

Solomon.

Bingi et al.

Support from

information

consultants and

expertise

Hwang et al., and

Mukherjee and

D’Souza.

Mabert et al.,

Bingi et al., H

Akkermans and

Helden, and Parr

and Shanks

Table 4.3

The figure below summarizes the idea in the table above. The x axis

represents the selected critical success factors. The y axis represents the

number of relevant research papers, which discuss the CSFs of data

warehouse and ERP technologies.

The white bar represents the research papers in the field of CSFs of ERP

system, which investigated the selected factors. The red bar represents the

research papers in the field of CSF of data warehouse technology, which

investigated the selected factors.

62

Figure 4.2

As mentioned earlier and based on the above table and figure, many research

papers have partly conferred about the organizational, project-related or

environmental dimensions (by discussing a factor or a couple under one

dimension or more). The main focus of these research projects was on the

technological and educational dimensions.

As observed, there were no research papers, or very few, that discussed

compatibility with partners and business competition as critical factors

influencing the adoption of data warehouse. This study digs deeper into these

two factors due to the following reasons:

• They identify key issues important to maintaining a competitive edge

of the enterprise in today’s highly competitive market.

• They stress the importance of having tight cooperation with direct

partners in different aspects of the supply chain.

The factors that will be investigated in the thesis are supposed to influence

data warehouse applications in pre-implementation and implementation

phases.

63

It is more important to consider the factors influencing data warehousing in

these two phases than it is to focus on the factors which influence the data

warehouse in the last phase (Post-implementation). In the first two phases, the

technical, organizational, environmental and operational backbones of data

warehousing are defined, planned and developed. On the other hand,

upgrading and modifying the system are the main activities in the post-

implementation phase. Therefore, it is important to identify the critical factors

that affect the activities in the first two phases.

4.6 Summary of the chapter A data warehouse is not just a software or simple project. It is a huge project,

which demands the coordination of a massive quantity of resources and

capacities and may last more than two years. Therefore it is crucial to be

aware of the critical issues which affect successful data warehousing

implementation before starting such a gigantic project.

This chapter clusters the knowledge of CSFs influencing a data warehouse

from the points of view of practitioners and academics to build the needed

backbone of the empirical research of this study. After that the critical factors

are classified into relevant phases of the data warehouse implementation

project. Finally, this chapter identifies the factors that will be investigated

later in the thesis.

64

5. Empirical research

5.1 Objective and structure

In this chapter, the empirical investigation is introduced. Research problems,

research model, proposed hypotheses, techniques used to extract and analyze

the data, and findings from the analysis are explained and discussed.

Section 5.2 indicates the research problem and objectives. In section 5.3, the

research models used throughout the study are presented and drawn. The

hypotheses of this thesis are developed in section 5.4. Methods and techniques

used to collect the data for data analysis and testing hypotheses are

highlighted in section 5.5. The data is analyzed and findings from research

methods are discussed in section 5.6. Finally, Section 5.7 investigates the

benefits gained from data warehouse, introduces the ranked list of CSFs and

discusses observations on the current status of data warehouses in the

investigated companies.

5.2 Research problem and objectives The adoption of data warehouse technology is costly and time-consuming

with high probability of failure, compared with other information technology

initiatives. Therefore, it is important to have a deeper understanding of the

factors which affect the adoption of data warehouse technologies.

The research problem of this thesis can be portrayed as “what are the Critical

Success Factors, under organizational, environmental and project-related

dimensions, which influence the adoption of data warehouse technology in

Finnish companies”.

5.3 Research model

65

To develop the research model, IT and data warehousing implementation and

success literature was reviewed to identify factors that affect data warehousing

success.

The proposed research model of this thesis groups the investigated critical

success factors into three dimensions (Organizational dimension,

Environmental dimension, and Project-related dimension). The key success

factors are classified under each dimension after studying the findings from

earlier research papers and using my educated guess.

Categorizing the relevant critical success factors into appropriate elements

facilitates showing the relationship among relevant factors and building the

proposed hypotheses for this thesis.

The figure below illustrates the research model of this study.

66

Figure 5.1

5.4 Hypotheses and variables 5.4.1 Organizational dimension

It is important for organizational factors to be understood by the decision

makers in order to overcome and reduce the barriers.

Champion

Top manag.

Business needs

Business competition

Vendor’s selection

Comp. with partners

Skilful team

Org. resources

End-user involv.

Consultants

Is the data warehouse a successful initiative or not?

H1.1

H1.2

H1.3

H2.2

H2.1

H2.3

H3.2

H3.1

H3.3

H3.4

Organizational

Environmental

Project-related

67

This dimension includes three factors (Existence of champions, Top

management sponsorship, and Business internal needs).

H1.1. Existence of champion

Previous research papers have indicated the positive influence of the existence

of the champion factor on successful implementation of data warehouse

technology.

Champions are the people inside the organization who appreciate and support

the adoption of new technology.

Champions play integral roles in providing necessary information, required

resources, needed assistance, political support and stimulating their associates

and staff to adapt the new technology.

This study believes that the existence of champion factor has a critical and

positive impact on the adoption of data warehouse technology.

H1.2. Top management sponsorship

Earlier studies have discussed largely the large positive influence of the Top

management sponsorship factor on successful adoption of data warehouse

technology.

The commitment of top management support is important to pass over sudden

barriers and complexities in data warehouse project. With top management

support the organization can secure required capital, human support,

cooperation and availability of other resources needed for the development

process.

This study builds the second hypothesis by assuming that the Top

management support factor has a great and positive influence in the adoption

of data warehouse technology.

H1.3. Business internal needs

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The alignment of data warehouse to business needs is a crucial step in data

warehouse adoption. It is important to clarify the strategic business objectives

and needs that a data warehouse would be expected to meet.

This study assumes that business internal needs have constructive and positive

influence in the adoption of data warehouse technology.

5.4.2 Environmental dimension

Environmental elements contribute largely to the success of data warehouse

technology. An enterprise is no longer able to maintain a competitive edge

without responding to challenges and changes resulting from the surrounding

environment. One possible solution, for responding to these challenges and

changes could be adapting powerful new technologies.

This dimension includes three factors (Business competition, Selection of

vendors, and Compatibility with partners).

H2.1 Extent of business competition

Enterprises often try to boost their competitive advantage and increase their

market share by adopting new information technology, especially if the

competitors have adopted this technology.

This study hypothesizes that the business competition factor influences

positively the successful adoption of data warehouse technology.

H2.2. Selection of vendors

Today’s organizations aim to outsource their business applications, the data

warehouse is one of them. As known, a data warehouse is a time-consuming

and very expensive system. Therefore companies must be aware while

selecting the vendors (implementation partner) and review carefully the

suggested plans. These plans might not be fully convenient for the company to

adapt them.

69

This study is aligned with the earlier studies in their belief that the selection of

vendors has a positive effect on the adoption of data warehouse technology.

H2.3. Compatibility with partners

Understanding partners’ systems and operations, and then reacting positively

could be the key subject to maintain long-term relationships with these

partners. A positive reaction could be visualized and actualized by adapting

compatible systems.

This study builds the sixth hypothesis by assuming that compatibility with

partners’ system has a positive impact on the adoption of data warehouse

technology.

5.4.3 Project-related dimension

The Project-related dimension is one of the foremost dimensions in the

adoption of data warehouse technology. Project-related factors are related to

project plan, analysis, development and control.

This dimension includes four factors; skills of project team, emergence and

coordination of organizational resources, end-user involvement, and support

from information consultants and expertise.

H3.1. Skills of project team

Project team members possessing strong knowledge of new technology and

better communication capability positively influence data warehouse

implementation, as shown in the previous studies. It is necessary to select the

members from different departments to add diverse values to the data

warehouse project. Providing relevant training courses to project team

members about technical, management and maintenance aspects is a very

important subject as well.

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This study assumes that the skills of project team factor affects greatly and

positively the adoption of data warehouse technology.

H3.2. Emergence and coordination of organizational resources

Data warehouses are high-priced, time-consuming and resource-intensive

initiatives. Therefore, having enough resources (people, money, and time) is a

prerequisite in the success of data warehouse projects. Coordination and

correct allocation of resources help the project team to finish the data

warehouse project on the proposed budget and on time.

This study builds the eighth hypothesis based on assuming that emergence and

coordination of organizational resources affects the adoption of data

warehouse technology positively.

H3.3. End-user involvement

Better user participation increases the probability of managing users’

expectations, satisfies their requirements and reduces their resistance to newer

technology. Previous investigators have stated that selection and inclusion of

fitting users in project teams is an important mission in the adoption of data

warehouse applications. Adequate training can help users to explore the

desirable information positively and in much more effective mode.

This study hypothesizes that end-user involvement has a positive impact on

the adoption of data warehouse technology.

H3.4. Support from outside consultants and expertise

The new appearance of data warehouse in the business field, coupled with

rapid growth of data warehouse market has led to the lack of competent and

qualified consultants. It is important and challenging to find experienced

consultants and keep them involved during and after the data warehouse

71

project. They provide professional insights and experiments to the adopters

for smooth running of the data warehouse project.

This study builds the last hypothesis by assuming that the support from

outside consultants factor greatly and positively influences the successful

adoption of data warehouse technology.

5.5 Data collection In this section, methods and techniques used to collect relevant data for study

analysis and testing the proposed hypotheses, are discussed and explained.

An emailed-questionnaire was used in this study to collect data from the

selected companies.

5.5.1 Questionnaire

5.5.1.1 Design of the questionnaire

In alignment with the research model, the questionnaire in this study was

designed based on reviewing prior related research questionnaires and

collecting professional insights.

To secure relevance, validity and reliability of this questionnaire a three-round

process of revision was formed.

The questionnaire was checked by my supervisor Mr. Anders Tallberg to

review each question and make necessary modifications. Then the

questionnaire was sent and further reviewed by a panel of PHD students.

Finally, the questionnaire got the approval from Mr. Anders Tallberg after his

second review and evaluation.

This questionnaire is composed of two sections:

• The first section is designed to collect basic data on respondents who

answer the questionnaire, and general data about their companies.

72

• The second section is the major part of the questionnaire. In this section,

data regarding critical success factors influencing data warehouse technology

in Finnish companies is collected. This section gathers, as well, data about the

obtainable benefits from adopting data warehouses in Finnish companies.

5.5.1.2 Objective of the questionnaire

The main objective of this study is to define the critical issues influencing the

adoption of data warehouse technology in Finnish companies. Therefore the

survey aimed to achieve a better understanding of these issues (critical

factors) by collecting relevant data for decent analysis and testing the

significance of the proposed hypotheses.

5.5.1.3 Sample description

The survey yielded results from Finland with respondents’ companies

crossing many industrial classifications.

As known, a Data warehouse is an expensive and time-consuming system,

which requires resources, expertise and capabilities. These resources and

expertise are used to afford huge set-up costs, dips in production (during and

after implementation phase), upgradeability and maintenance expenses. Mid-

and large- sized companies are the only ones that possess enough capabilities

to afford data warehouses. Consequently, the questionnaire was steered

toward mid- and large-sized companies.

In order to achieve the thesis objectives, a focused survey was conducted and

geared toward certain titles of posts such as Chief Information officers (CIO),

Chief Financial Officers (CFO), IT administrators and other similar titles.

The reasons behind selecting such people are as follow:

• These people are mostly involved and assigned as project team leaders in

data warehouse projects.

• They interact daily or regularly with data warehouse technology for varied

purposes.

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• These people have strategic and managerial levels in their organizations

with relevant educational background (bachelor’s degree and above) and

broad decision making capabilities. Therefore, some of these people might be

the champions of data warehouse technology projects in their companies.

After a three-round process of checking and reviewing the questionnaire, a

total of 220 questionnaires were e-mailed to the targeted delegates at the

selected companies. The companies were identified via a computer search of

Hanken’s financial database (Voitto). This database lists companies and their

basic information (their trade name, their website address, annual turnover and

so on) based on certain metrics (criteria). The sample was selected based on

their annual turnover (the companies which had a turnover of more than

25000000€ last-year).

The original e-mailed questionnaire was followed by a three-round process of

sending solicitation (reminders) to remind the delegates to fill out the

questionnaire.

5.6 Data analysis and discussion of research results 5.6.1 Analysis of data gained via questionnaire

A final of eighteen responses to the questionnaire were received after a period

of more than two months. All of the survey responses are valid and utilizable

except for some questions within a response, which were answered by N/A

(No Answer).

The resulting response rate was 8% after sending the original e-mailed

questionnaire and performing a three-round process of mailing solicitation

(reminders) to targeted employees.

The response rate was quite low for the following reasons:

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• The limited existence of mid- and large-sized companies in the Finnish

market. As mentioned earlier, those companies are the most capable ones to

afford the adoption of data warehouses.

• The lack of comprehensive understanding and knowledge regarding data

warehouse technology due to the recent appearance of this technology in the

business field.

• The questionnaires and reminders were e-mailed to the companies during

June and July. Those two months are well-known as the season of vacations in

Finland. Consequently, I got a lot of auto replies to my e-mails from the

delegates, saying they were out of their offices for work or vacation-related

reasons

The parts below are the analysis of results obtained from the questionnaires.

5.6.1.1 Analysis of the first section of the questionnaire

The first section in the questionnaire was designed to collect basic data on the

respondents, who answer this questionnaire, and general data about their

companies.

This section contains a mix of multiple choice and open-ended questions. The

open-ended questions were designed to remove the impression of restricting

respondents with predetermined choices.

5.6.1.1.1 Title of post of respondent

To secure validity, relevance and reliability of data for analysis, it is important

to ensure the relevance of respondents’ backgrounds (educational and work-

related background). The respondent should be an IT- and data warehouse-

savvy person and have regular interaction with a data warehouse. Such a

person can be in the following positions CIO, CFO and IT administrator.

The figure below shows the distribution of respondent’s title. The axis (x)

represents the title of the post of the respondent and the axis (y) represents the

percentage.

75

Title distribution

17%

33%28%

22%

00,050,1

0,150,2

0,250,3

0,35

CIO CFO IT admninistrator Other

Title

Per

cent

age

Figure 5.2

As noticed from the above figure, 33% of the respondents (6 respondents)

were CFOs at their companies. 28% of the respondents (5 respondents) were

IT administrators at their companies. 17% of the respondents (3 respondents)

were CIOs at their companies. 22% of the respondents (4 respondents) were

playing different roles in strategic and managerial levels at their companies,

such as production director (1 respondent), logistic director (1 respondent),

corporate advisor (1 respondent), and solution owner (1 respondent).

5.6.1.1.2 Last year’s turnover

Data warehouses are mostly adopted by mid- and large-sized companies,

because, as mentioned earlier, these companies are the most competent ones

to overcome the obstacles presented by data warehouse adoption.

This question is included in the questionnaire to measure the size of the

company in terms of annual turnover.

The table below illustrates the sizes of the companies measured by last year’s

turnover.

76

Last year’s turnover Percentage

25000000€ – 100000000€ 6%

100000000€ – 500000000€ 44%

500000000€ – 1000000000€ 22%

More than 1000000000€ 28%

Table 5.1

As noticed, 44% of the responses (8 responses) received from companies

reported last year’s revenue between 100000000€ - 500000000€. 28% of the

responses (5 responses) received from companies reported last year’s revenue

more than 1000000000€. 22% of the responses (4 responses) received from

companies reported last year’s revenue between 500000000€ - 1000000000€.

6% of the responses (1 response) received from companies reported last year’s

revenue between 25000000€ - 100000000€.

5.6.1.1.3 Type of industry in which the company incorporates

This question aims to investigate types of industries, which use data

warehouse technology. This question can be applied as well to digging deeper

into identifying the industries which use data warehouses extensively (with

high percentage) and the ones, which use this technology narrowly (with low

percentage).

The table below indicates the types of industries cited in the responses and the

number of responses for each type of industry.

Type of industry Number of responses

Graphic Business 1

Steel production 2

Paper and pulp production 3

Mechanics and electronics 1

Service 3

Technical wholesale 1

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Beverages 1

Consumer Discretionary 1

Food production 2

Pharmaceutical wholesale 1

Software and network 1

Machinery rental 1

Table 5.2

I reclassified the aforementioned industries into bigger categories. This

classification was made based on finding common functional characteristics

among the smaller ones. Examples, The companies, which produce tangible

products, are classified under production industry. The sorting facilitates

analyzing the industries and supports the identification of industries which

widely or narrowly adopt data warehouse, as shown in the figure below.

Industry distribution

61%

11%

28%

0

0,10,2

0,3

0,4

0,50,6

0,7

Production industry Wholesale industry Service industry

Industry

Perc

enta

ge

Figure 5.3

As observed, 61% of the responses (11 responses) were received from

companies producing and manufacturing tangible products to customers. 28%

of the responses (5 responses) were received from companies producing

intangible products (services) to clients. 11% of the responses (2 responses)

were received from wholesalers in the Finnish market.

78

5.6.1.1.4 Year of data warehouse installation

It is relevant to this study to know the year when data warehouse technology

was installed in the companies investigated. This question intends to explore

the maturity level of data warehousing in companies, i.e. young or mature data

warehouse.

The table below illustrates the year of data warehouse installation and the

number of responses for each year.

Year of installation Number of responses

1991 1

1999 3

2000 2

2001 4

2002 2

2003 1

2004 3

2005 1

Continuous development process 1

Table 5.3

As shown by the above table, most of the companies installed their data

warehouses during the last 5 years. The short-term deployment of data

warehouse technology leads to the following conclusion: The investigated

companies, in particular and Finnish companies in general, do not have

enough experience in data warehousing initiatives.

5.6.1.1.5 Name of supplier (vendor) of current data warehouse technology

This question investigates the name of the supplier of the current data

warehouse, used at the respondents’ companies.

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The figure below shows the vendor distribution, where you can find the

vendor’s name and the percentage.

Vendor distribution

39%

22%

11%17%

11%

00,050,1

0,150,2

0,250,3

0,350,4

0,45

SAP Oracle Cognos Other Mix

Vendor

Perc

enta

ge

Figure 5.4

As noticed, SAP is the dominant brand name, as a data warehouse solution

provider, in the Finnish market with 39% of the responses (7 responses).

Oracle has the second largest market share, as a data warehouse technology

provider, with 22% of the responses (4 responses). 11% of the responses (2

responses) received from companies use Cognos’s data warehouse solution.

11% of the responses (2 responses) received from companies use data

warehouse solutions from different suppliers (more than one DW provider).

17% of the respondents’ companies (3 responses) use data warehouses

supplied from other suppliers, such as Datium (1 response), and e-big (1

response), and use self-made data warehouse (1 response).

5.6.1.1.6 Previous data warehouse installed and used

This question is answered only by the companies, which have renewed their

data warehouse technology recently. The reasons behind the replacement

might be related to efficiency matters, or upgradeability to newer versions or

overcoming problems experienced in the previous system.

80

Based on the responses, 78% of the respondents (14 responses) answered this

question “NO”, i.e. their companies didn’t change their data warehouse

technologies. 22% of the respondents’ companies (4 responses) have changed

their data warehouse technology due to different reasons. The following are

the reasons behind changing the previous data warehouse, as stated by the

respondents:

• Moving from one vendor to another for more flexibility, efficiency and

automation of data storage, analysis and reporting

• Moving from department-level to enterprise-level data warehouses

• Upgradeability to newer versions.

5.6.1.1.7 The data warehouse type

This question aims to explore the types of data warehouse technology in the

respondents’ companies.

The figure below highlights the data warehouse type distribution. The x axis

represents the data warehouse types and the y axis represents the percentage.

DW Type distribition

78%

22%

00,10,20,30,40,50,60,70,80,9

Enterprise-level Department-level

DW type

Per

cent

age

Figure 5.5

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As observed, 78% of the respondents’ companies (14 responses) installed

enterprise-level data warehouse. 22% of the respondents’ companies (4

responses) installed department-level data warehouse.

5.6.1.1.8 Degree of complexity of the data warehouse project

This question intends to explore the degree of complexity of the data

warehouse adoption project in the respondents’ companies. This question is a

multiple choice question, which has the following predetermined answers: not

complex, weakly complex, quite complex, complex, very complex.

The figure below highlights the complexity distribution. The x axis represents

the complexity degree and the y axis represents the percentage.

Complexity distribution

0%6%

22%

44%

28%

0

0,1

0,2

0,3

0,4

0,5

Notcomplex

Weaklycomplex

Quitecomplex

Complex Verycomplex

Complexity degree

Perc

enta

ge

Figure 5.6

As shown by the above figure, 44% of the respondents (8 responses)

considered the data warehouse project as a complex project. 28% of the

respondents (5 responses) considered the data warehouse project as a very

complex one. 22% of the respondents (4 responses) thought that it is a quite

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complex project. 6% of the respondents (1 response) thought that it is a

weakly complex project.

5.6.1.1.9 The company size (measured by number of employees)

This question is applied to measure the size of the respondents’ companies.

This question goes hand in hand with the last year’s turnover question for

defining the size of the organization.

5.6.1.2 Analysis of the second section of the questionnaire

The second section is the major part of the questionnaire. In this section, the

data regarding critical success factors as well as data about the benefits gained

from data warehouses in Finnish companies is gathered and collected.

This section includes a six-scale method of ranking the contribution of key

success factors. (1- Not important. 2- Weakly important. 3- Quite important.

4- Important. 5- Very important. N/A).

This part of the analysis aims to analyze the data gathered from the second

section of questionnaire, to test the significance of proposed hypotheses.

5.6.2.2.1 Existence of champions

The existence of champions factor has a crucial impact on the embracing of

data warehouse technology. They play an integral role in providing necessary

information, required resources, needed assistance, political support and

stimulating the staff to adopt new technology.

The figure below illustrates the importance distribution of the existence of

champion factor. The x axis represents the importance degree and the y axis

represents the percentage.

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Importance distribution

11%

0%6%

50%

33%

0%0

0,1

0,2

0,3

0,4

0,5

0,6

Not imp. Weaklyimp.

Quiteimp.

Important Veryimp.

N/A

Importance degree

Perc

enta

ge

Figure 5.7

As observed, 50% of the respondents (9 responses) ranked the existence of

champions factor as an important factor. 33% of the respondents (6 responses)

ranked this factor as a very important factor. 11% of the respondents (2

responses) ranked this factor as a not important factor. 6% of the respondents

(1 response) ranked it as a quite important factor.

Based on the above analysis, 83% (Important + Very important) of the

respondents believed that the existence of champions factor is a critical factor

influencing data warehouse technology. On the other hand, 11% of the

respondents believed that the existence of champions factor is not a critical

factor. It seems that the data supports strongly the first hypothesis (The

existence of the champion has a critical and positive impact on the

adoption of data warehouse technology in the Finnish companies).

5.6.2.2.2 Top management sponsorship

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The commitment of top management support is very important to pass over

sudden barriers and complexities faced from adopting data warehouse

technology.

The figure below shows the importance distribution of the top management

sponsorship factor. The x axis represents the degree of importance and the y

axis represents the percentage.

Importance distribution

6%0%

11%

39%44%

0%0

0,1

0,2

0,3

0,4

0,5

Not imp. Weaklyimp.

Quiteimp.

Important Very imp. N/A

Importance degree

Perc

enta

ge

Figure 5.8

As shown by the above figure, 44% of the respondents (8 responses)

considered the top management sponsorship factor as a very important factor.

39% of the respondents (7 responses) considered this factor as an important

factor. 11% of the respondents (2 responses) considered this factor as a quite

important factor. 6% of the respondents (1 response) considered this factor as

a not important factor.

Based on the above analysis, 83% (Very important + important) of the

respondents evaluated the top management sponsorship factor as a critical

factor that impacts the success of data warehouse technology. On the other

side, 6% of the respondents evaluated the top management support factor as a

85

not important factor. As a result, The data seems to validate strongly the

second hypothesis. (The Top management has a great influence in the

adoption of data warehouse technology in the Finnish companies).

5.6.2.2.3 Business internal needs

The alignment of a data warehouse to business needs is a crucial step in a data

warehouse adoption project. Before starting such a gigantic effort it is

important to clarify strategic business objectives and needs that a data

warehouse would be expected to meet.

The figure below illustrates the importance distribution of the business

internal needs factor. The x axis represents the importance degree and the y

axis represents the percentage.

Importance distribution

11%

0%

22%

28%33%

6%

00,05

0,10,15

0,20,25

0,30,35

Not imp. Weaklyimp.

Quiteimp.

Important Very imp. N/A

Importance degree

Perc

enta

ge

Figure 5.9

As indicated by the above figure, 33% of the respondents (6 responses)

considered the business internal needs factor as a very important factor. 28%

of the respondents (5 responses) considered this factor as an important factor.

22% of the respondents (4 responses) considered this factor as a quite

86

important factor. 11% of the respondents (2 responses) considered this factor

as a not important factor. 6% of the respondents (1 response) didn’t have an

answer to this question.

Based on the analysis, 61% (very important + important) of the respondents

evaluated the business internal needs factor as a critical factor influencing the

adoption of data warehouse technology. On the other hand, 11% of the

respondents believed that the business internal needs factor doesn’t affect the

success of the data warehouse. Hence, the data supports the third hypothesis

(The business internal needs have a constructive influence in the adoption

of data warehouse technology in the Finnish companies).

5.6.2.2.4 Selection of vendors

Companies must be aware while selecting vendors, because the data

warehouse project is a huge and risky project. The plans suggested by vendors

may not be completely convenient for an enterprise itself. Consequently, the

enterprise can’t adapt whatever is recommended and suggested by the

vendors. The figure below indicates the importance distribution of the

selection of vendors factor. The x axis represents the importance degree and

the y axis represents the percentage.

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Importance distribution

6% 6%

38%33%

17%

0%0

0,050,1

0,150,2

0,250,3

0,350,4

Not imp. Weaklyimp.

Quiteimp.

Important Very imp. N/A

Importance degree

Perc

enta

ge

As observed, 38% of the respondents (7 responses) were neutral in their

opinions toward ranking the importance of selecting appropriate vendors in

successful data warehouse project. 33% of the respondents (6 responses)

identified this factor as an important factor. 17% of the respondents (3

responses) identified this factor as a very important factor. 6% of the

respondents (1 response) identified this factor as a weakly important factor.

6% of the respondents (1 response) identified this factor as a not important

factor.

Based on the analysis, 50% (very important + important) of the respondents

assessed the good selection of vendors as a crucial factor for successful

adoption of data warehouse technology. Alternatively, 12% (weakly important

+ not important) of the respondents agreed that the good selection of vendors

is not a critical aspect in successful data warehousing. Consequently, the data

is believed to support fairly the fourth hypothesis (There is a positive

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correlation between the business competition and the successful

implementation of data warehouse technology).

5.6.2.2.5 Compatibility with partners

It is important to understand the systems adapted by direct partners in the

supply chain. This leads to facilitate exchange of knowledge and information

between Supply Chain members, which will enhance the performance and the

profit of overall supply chain members.

The figure below indicates the importance distribution of the compatibility

with partners factor. The x axis represents the importance degree and the y

axis represents the percentage.

Importance distribution

17%

50%

22%

6% 6%0%

0

0,1

0,2

0,3

0,4

0,5

0,6

Not imp. Weaklyimp.

Quiteimp.

Important Very imp. N/A

Importance degree

Perc

enta

ge

Figure 5.10

As shown by the above figure, 50% of the respondents (9 responses) thought

that compatibility with partners systems has a weak impact on successful

implementation of data warehouse technology. 22% of the respondents (4

responses) thought that this factor is a quite important factor. 17% of the

89

respondents (3 responses) thought that this factor is not important factor and

doesn’t affect the adoption of data warehouse applications. 6% of the

respondents (1 response) believed that this factor is a very important factor.

6% of the respondents (1 response) believed that this factor is an important

factor.

Based on the above analysis, 67% (not important + weakly important) of the

respondents assessed the compatibility with partners factor as a non-critical

factor. 12% (very important + important) of the respondents considered the

compatibility with partners factor as a critical factor. As a result, It looks like

the data dose not support the fifth hypothesis (The selection of vendors has a

positive effect in the adoption of data warehouse technology).

5.6.2.2.6 Extent of business competition

The organization can no longer maintain the piloting edge in its industry

without the adoption of new technology, especially if the competitors are

adopting or have adopted this technology.

The figure below highlights the importance distribution of the extent of

business competition factor. The x axis represents the importance degree and

the y axis represents the percentage.

90

Importance distribution

23%

33% 33%

0%

11%

0%0

0,05

0,10,15

0,20,25

0,30,35

Not imp. Weaklyimp.

Quiteimp.

Important Very imp. N/A

Importance degree

Perc

enta

ge

Figure 5.11

The figure above proves that 33% of the respondents (6 responses) assessed

the business competition factor as a quite important factor. 33% of the

respondents (6 responses) assessed this factor as a weakly important factor.

23% of the respondents (4 responses) assessed this factor as a not important

factor. 11% of the respondents (2 responses) assessed this factor as a very

important factor.

As observed, 66% (not important + important) of the respondents agreed that

the business competition factor is a non-critical factor influencing the success

of data warehouse. On the other side, 11% of the respondents assessed this

factor as a critical factor. As a result, the data is deemed not to support the

sixth hypothesis (The compatibility with partners’ system has a positive

impact in the adoption of data warehouse technology).

5.6.2.2.7 Skills of project team

91

The skills of the project team have an endless impact on the smooth running

of a data warehouse project.

The figure below shows the importance distribution of the skills of project

team factor. The axes x represents the importance degree and the axes y

represents the percentage

Importance distribution

6%0%

11%

66%

17%

0%0

0,1

0,20,30,40,5

0,60,7

Not imp. Weaklyimp.

Quiteimp.

Important Very imp. N/A

Importance degree

Perc

enta

ge

Figure 5.12

The figure demonstrates that 66% of the respondents (12 responses) evaluated

the skills of project team factor as an important factor. 17% of the respondents

(3 responses) evaluated this factor as a very important factor. 11% of the

respondents (2 responses) evaluated this factor as a quite important factor. 6%

of the respondents (1 response) evaluated this factor as a not important factor.

Based on the above analysis, 83% (very important + important) of the

respondents believed that having a skilful team is a critical factor that affects

the success of data warehouse technology. On the other hand, 6% of the

respondents believed that the skills of project team factor is not a critical

92

factor. Therefore, It looks like the data strongly validates the seventh

hypothesis (The skills of project team effects greatly and positively the

adoption of data warehouse technology).

5.6.2.2.8 Availability and Coordination of organizational resources

Availability of enough resources (people, money, and time) and allocating

them correctly in a data warehouse project are necessary requirements.

The figure below shows the importance distribution of the availability and

coordination of organizational resources factor. The x axis represents the

importance degree and the y axis represents the percentage.

Importance distribution

6%0%

22%28%

44%

0%0

0,050,1

0,150,2

0,250,3

0,350,4

0,450,5

Not imp. Weaklyimp.

Quiteimp.

Important Very imp. N/A

Importance degree

Perc

enta

ge

Figure 5.13

As noticed, 44% of the respondents (8 responses) considered the availability

and coordination of organizational resources factor as a very important factor.

28% of the respondents (5 responses) considered this factor as an important

factor. 22% of the respondents (4 responses) considered this factor as a quite

93

important factor. 6% of the respondents (1 response) considered this factor as

a non-important factor.

Based on the above discussion, 72% (very important + important) of the

respondents evaluated the availability and coordination of organizational

resources factor as a necessary and critical factor. Alternatively, 6% of the

respondents evaluated the availability and coordination of organizational

resources is not important factor. Hence, it seems that the data is aligned with

the eighth hypothesis (The emergence and coordination of organizational

resources affects the adoption of data warehouse technology positively in

the Finnish companies).

5.6.2.2.9 Support from outside consultants

The consultants, who possess much experience, are employed to provide ideas

and lend a hand to the organizations that lack the experience to adopt, install

and maintain a new information technology.

The figure below highlights the importance distribution of the support from

outside consultants. The x axis represents the importance degree and the y

axis represents the percentage.

94

Importance distribution

17%

0%

28%

38%

17%

0%0

0,050,1

0,150,2

0,250,3

0,350,4

Not imp. Weaklyimp.

Quiteimp.

Important Very imp. N/A

Importance degree

Perc

enta

ge

Figure 5.14

As indicated, 38% of the respondents ranked the support from an outside

consultant factor as an important factor. 28% of the respondents ranked this

factor as a quite important factor. 17% of the respondents ranked this factor as

a very important factor. 17% of the respondents ranked this factor as a non-

important factor.

Based on the above discussion, 55% (very important + important) of the

respondents evaluated the support from outside factor as an essential factor

influencing successful adoption of data warehouse. On the other side, 17% of

the respondents assessed the support from outside consultants as a not

important factor. Therefore, it looks like the data supports fairly the ninth

hypothesis (The support from outside information consultants and

expertise influences greatly and positively the successful adoption of the

data warehouse technology).

5.6.2.2.10 End-user involvement

95

Better user participation increases the probability of managing users’

expectations, reduces their resistance and satisfies user requirements.

The figure below highlights the importance distribution of the end-user

involvement factor. The x axis represents the importance degree and the y axis

represents the percentage.

Importance distribution

6%0%

39%44%

11%

0%0

0,050,1

0,150,2

0,250,3

0,350,4

0,450,5

Not imp. Weaklyimp.

Quiteimp.

Important Very imp. N/A

Importance degree

Perc

enta

ge

Figure 5.15

As shown by the above figure, 44% of the respondents believed that the user-

involvement factor is an important factor. 39% of the respondents believed

that this factor is a quite important factor. 11% of the respondents believed

that this factor is a very important factor. 6% of the respondents believed that

this factor is a non-important factor.

Based on the above analysis, 55% of the respondents assessed the user-

involvement factor as a critical factor affecting the success of data warehouse

technology. On the other hand, 6% of the respondents considered the user-

involvement factor as a not critical factor. Consequently, the data endorses

96

fairly the last hypothesis (The End-user involvement has a positive impact

on the adoption of the data warehouse technology).

5.7 General analyses Under this section advanced investigations are going to be held regarding the

benefits gained from the installation of data warehouse technology in Finnish

companies. Then the ranked list of critical success factors and the

observations of current status related to data warehouse adoption are

presented.

5.7.1 Product profitability

This part intends to discover the value added to the product due to data

warehouse adoption.

In the figure below, the x axis represents the importance degree and the y axis

represents the percentage.

Importance distribution

22%

17%

28% 28%

0%

6%

0

0,05

0,1

0,15

0,2

0,25

0,3

Not imp. Weakly imp. Quite imp. Important Very imp. N/A

Importance degree

perc

enta

ge

Figure 5.16

97

As noticed, 28% of the respondents (5 responses) considered that it is

important to have a data warehouse for increasing the product profitability.

28% of the respondents (5 responses) were neutral in their opinion about the

contribution of a data warehouse in product profitability by selecting the quite

important alternative. 22% of the respondents (4 responses) realized that a

data warehouse doesn’t affect product profitability. 17% of the respondents (3

responses) realized that a data warehouse has a weakly important role in

increasing the product profitability. 6% of the respondents (1 response) didn’t

have answer for this question.

Based on the analysis, 39% (not important + weakly important) of the

respondents believed that adapting a data warehouse doesn’t enhance the

product profitability. On the other hand, 28% of the responses believed that it

is important to have a data warehouse to increase the product profitability. As

a result, data warehouse technology is not an important element to increase

the product profitability in Finnish companies.

5.7.2 Customer profitability

This part collects the insights of the respondents about the effect of data

warehouse technology on customer profitability.

Data warehouse technology (as a data repository stores, analyses and reports

the needed information accurately and in-time) can affect largely the overall

performance of the supply chain. This effect can be measured and noticed by

increasing the availability and the accessibility of relevant and important data

in real-time. This functions to reduce production cycle, lessen expenses,

increase product quality and maximize the profit of overall supply chain

members (suppliers and customers).

In the figure below, the x axis represents the importance degree and the y axis

represents the percentage.

98

Importance distribution

11%

22%

33%

28%

0%

6%

0

0,05

0,1

0,15

0,2

0,25

0,3

0,35

Not imp. Weakly imp. Quite imp. Important Very imp. N/A

Importance degree

perc

enta

ge

Figure 5.17

As indicated by the figure, 33% of the respondents (6 responses) thought that

it is quite important to install a data warehouse for increasing customer

profitability. 28% of the respondents (5 responses) believed that a data

warehouse is an important tool for increasing customer profitability. 22% of

the respondents (4 responses) believed that it is weakly important to install a

data warehouse for maximizing customer profitability. 11% of the respondents

(2 responses) thought that there is no need for a data warehouse to increase

customer profitability. 6% of the respondents (1 response) didn’t have an

answer.

Based on the discussion, 33% (weakly important + not important) of the

respondents considered the existence of data warehouse applications not to be

an important component in increasing customer profitability. Conversely, 28%

of the respondents thought that having a data warehouse is an important aspect

in increasing customer profitability.

99

Thus, Data warehouse technology is not important to raise the customer

profitability in Finnish companies.

5.7.3 Employee profitability

In this part, the effect of installing data warehouse on increasing the employee

profitability is introduced.

These benefits can be measured through increasing the employees’

willingness and satisfaction toward a data warehouse.

In the figure below, the x axis represents the importance degree and the y axis

represents the percentage.

Importance distribution

11%

33%

22%

28%

0%

6%

0

0,05

0,1

0,15

0,2

0,25

0,3

0,35

Not imp. Weakly imp. Quite imp. Important Very imp. N/A

Importance degree

perc

enta

ge

Figure 5.18

As observed, 33% of the respondents (6 responses) deemed that a data

warehouse is a weakly important tool for increasing employee profitability.

28% of the respondents (5 responses) deemed that a data warehouse is an

important tool for increasing employee profitability. 22% of the respondents

100

(4 responses) ranked the existence of data warehouse as a quite important

component. 11% of the respondents (2 responses) believed that there is no

association between installing the data warehouse and increasing employee

profitability. 6% of the respondents (1 response) didn’t have answer.

Based on the discussion, 44% (weakly important + not important) of the

respondents believed that it is not important to have a data warehouse in order

to boost employee profitability. On the other hand, 28% of the respondents

thought that it is important to have a data warehouse to enhance employee

profitability.

Therefore, having a data warehouse does not affect the employee profitability

in Finnish companies.

5.7.4 Branch profitability

In this part, the following question is going to be answered: Does a data

warehouse affect the branch profitability in terms of increasing the profits and

reducing the expenses if a branch of a complex organization has adopted this

technology?

In the figure, the x axis represents the importance degree and the y axis

represents the percentage

101

Importance distribution

28%

22%

39%

6%

0%

6%

0

0,05

0,1

0,15

0,2

0,25

0,3

0,35

0,4

0,45

Not imp. Weakly imp. Quite imp. Important Very imp. N/A

Importance degree

perc

enta

ge

Figure 5.19

As noticed, 39% of the respondents (7 responses) assessed the contribution of

data warehouse technology in increasing the branch profitability as a quite

important element. 28% of the respondents (5 responses) believed that there is

no need for a data warehouse to increase the branch profitability. 22% of the

respondents (4 responses) assessed the data warehouse technology as a weakly

important tool for increasing the branch profitability. 6% of the respondents (1

response) agreed that data warehouse technology is an important tool for

increasing the branch profitability. 6% of the respondents (1 response) didn’t

have answer.

As indicated in the figure, 50% (weakly important + not important) of the

respondents agreed that having data warehouse applications doesn’t raise the

branch profitability. On the other side, 6% of the respondents thought that it is

important to have a data warehouse to increase the branch profitability.

102

Therefore, owning a data warehouse doesn’t increase the branch profitability

of a complex Finnish company.

5.7.5 Productivity

Productivity is a measure of efficiency and is usually considered as output per

person-hour, or the amount of output per unit of input (labor, equipment, and

capital) used in accomplishing the assigned task. It is measured as a ratio of

output per unit of input over time.

In the figure below, the x axis represents the importance degree and the y axis

represents the percentage

Importance distribution

11% 11%

28%

44%

0%

6%

0

0,05

0,1

0,15

0,2

0,25

0,3

0,35

0,4

0,45

0,5

Not imp. Weakly imp. Quite imp. Important Very imp. N/A

Importance degree

perc

enta

ge

Figure 5.20

As highlighted by the figure, 44% of the respondents (8 responses) evaluated

a data warehouse as an important technique to maximize the productivity.

28% of the respondents (5 responses) evaluated a data warehouse as a quite

important factor to maximize the productivity. 11% of the respondents (2

103

responses) believed that the productivity is not affected positively by the

installation of data warehouse technology. 11% of the respondents (2

responses) evaluated the data warehouse as a weakly important technique to

increase the productivity. 6% of the respondents (1 response) didn’t have an

answer.

As observed, 44% of the respondents agreed that possessing a data warehouse

is important to increase the productivity. Where as 22% (not important +

weakly important) of the respondents believed that data warehouses don’t

affect the productivity.

Therefore, data warehouse technology is a critical aspect to increase the

productivity in Finnish companies.

5.7.6 Customer satisfaction

This part aims to discover the value added to the customer satisfaction due to

adopting a data warehouse technology.

The customer satisfaction can be quantified by increasing the loyalty of the

customers and their willingness to keep a relationship with the company.

In the figure below, the x axis represents the importance degree and the y axis

represents the percentage.

104

Importance distribution

22%

28%

22%

11% 11%

6%

0

0,05

0,1

0,15

0,2

0,25

0,3

Not imp. Weakly imp. Quite imp. Important Very imp. N/A

Importance degree

perc

enta

ge

Figure 5.21

Based on the above figure, 28% of the respondents (5 responses) evaluated a

data warehouse as a weakly important tool to increase customer satisfaction.

22% of the respondents (4 responses) believed that the existence of a data

warehouse doesn’t affect customer satisfaction. 22% of the respondents (4

responses) assessed data warehouse technology as a quite important tool. 11%

of the respondents (2 responses) assessed a data warehouse as an important

tool. 11% of the respondents (2 responses) assessed a data warehouse as a

very important element in customer satisfaction. 6% of the respondents (1

response) didn’t have answer.

As noticed, 50% (weakly important + not important) of the respondents

looked at the data warehouse technology as not an important element in

increasing customer satisfaction. Where as 22% (very important + important)

of the respondents looked to data warehouse as an important element in

increasing customer satisfaction.

As a result, a data warehouse does not effect customer satisfaction in Finnish

companies.

105

5.7.7 List of critical success factors and discussion of observations:

The Table below introduces the ranked list of critical success factors, which is

rated by the respondents in the investigated companies. The factors were rated

based on the respondents’ estimations as to what extent these factors influence

the adoption of data warehouse technology. The rankings of factors were

made based on the ratings in the important column. The rankings start with the

factor that has the highest rating and end with the one that has the lowest

rating in the important column.

The ratings in the important column were the results of the addition of the

very important rating and the important rating for each factor. If two factors

have the same rating in the important column, then the criteria used to select

the higher one is which of the two has the greater very important rating. For

more clarity in this regard see key words and explanations below the table.

Factor Important Quite important Not Important N/A

Top management 83% 11% 6% 0%

Champions 83% 6% 11% 0%

Skilful project team 83% 11% 6% 0%

Availability of resources 72% 22% 6% 0%

Business internal needs 61% 22% 11% 6%

Outside consultants 55% 28% 17% 0%

End-user involvement 55% 39% 6% 0%

Selection of vendors 50% 38% 12% 0%

Compatibility with partners 12% 22% 67% 0%

Business competition 11% 33% 56% 0%

Table 5.4 Key words: Important = Very important + Important

Not important = Weakly important + Not important

Explanation:

106

As noticed, Top management support, Existence of champion and skills of

project team have similar ratings, which is the highest rating, but top

management was ranked as first among the investigated critical factors.

The main reason is that the Top management support factor has the highest,

very important rating (44%).

Support from an outside consultant and end-user involvement have the same

rating as well, but support from an outside consultant was ranked higher than

the End-user involvement factor.

The reason is that the Support from an outside consultant factor has higher,

very important, rating (17%) than the End-user involvement factor.

This table summarizes the findings and results from the second section of the

questionnaire.

5.7.7.1 Discussion about the Findings in the organizational dimension

The findings in this study are in line with the findings from preceding

empirical research in regard of Top management support. Top management

sponsorship was cited as a key factor affecting the adoption of new

technology in many research projects, theoretical (Nah et al. 2001), (Bingi et

al. 1999), (Mukherjee and D’Souza, 2003) and empirical ones (Wixom and

Watson, 2001), (Hwang et al. 2004), (Watson et al. 2002), (Mabert et al.

2001), (Akkermans and Helden, 2002), (Umble et al. 2003), (Parr and Shanks,

2000).

This study believes that greater top management support will lead to more

resources and capital (money, time, and labor) to adopt data warehouse

technology. As a result, the support from top management will be a strong

sign that the adoption of data warehouse technology will go smoothly.

The lack of this significant factor will lead to loss of the assistance needed to

obtain the required resources, and thus negatively effect the adoption of data

warehouse applications.

107

Based on the results from the questionnaire, the top management support

factor was ranked as the most critical factor among the key factors

investigated in the thesis.

Champions are people from inside the organization, who appreciate the

contribution of new technology and convince the staff and even their superiors

to adopt new technology. Consequently, the existence of champions should

greatly effect the adoption of new technology as cited by former, related

research, empirical (Wixom and Watson, 2001), (Hwang et al. 2004), (Watson

et al. 2002), (Akkermans and Helden, 2002), (Parr and Shanks, 2000) and

theoretical ones (Nah et al. 2001).

The existence of champions factor was ranked as the second most important

factor, which influences the adoption of data warehouse technology in Finnish

companies. Hence, the results of this study validate the belief in the great

contribution of this factor as has prior research.

As indicated by the earlier studies, internal needs stimulate organizations to

find good solutions. A data warehouse is the best solution for companies,

which strive to have enough relevant, easy-to-access, reliable and real-time

data around the clock, stored in one place. This data repository would have a

large positive effect on the analysis process, which leads to better decision

making initiatives, as cited in the prior research papers, both empirical

(Mukherjee and D’Souza, 2003; Joshi and Curtis, 1999) and theoretical

(Hwang et al., 2004; Watson et al. 2002).

The internal needs factor was ranked in the fifth among the factors, which

affect the adoption of the data warehouse in the Finnish companies. Therefore,

the result from this study is aligned with the previous studies.

5.7.7.2 Discussion about the findings in the environmental dimension

An enterprise cannot longer maintain its competitive advantage and not be a

star performer in its industry without responding effectively and efficiently to

108

the myriad challenges of the market. One technique could be adapting

powerful information technologies. Theoretically, if you as a company face

severe competition in the market you must react quickly to maintain your

market share. Otherwise, you will lose your seat among the market leaders.

Therefore, if the competitors have installed powerful technologies to increase

their market share you must adopt at least the same level of information

technology, especially in the keen competition and highly computerized

advancement of today’s markets.

In this study, the findings indicated that the degree of business competition

factor does not affect the adoption of data warehouse technology in Finnish

markets, not as found in prior research papers (Hwang et al. 2004).

The reasons would be that data warehouse technology is not widely used by

Finnish companies, due to its expensive and risky nature. In addition, the

obtainable benefits from using it are intangible benefits (not easily quantified)

or need some time to be realized. For these reasons, the companies can not

easily recognize the benefits from using this technology in the short-run.

Good selection of vendors has a positive impact on the process of adopting

data warehouse technology. Vendors provide the company with products,

expertise, and required technological abilities to facilitate the adoption

process.

The vendors can not do the work without the assistance of internal expertise.

Thus, integration must be established between the outside expertise

represented by the implementation partner and the in-house expertise

represented by functional employees.

In this study, the selection of vendors has a fair impact on the adoption of data

warehouse technology in Finnish companies. This study agrees with prior

studies, both theoretical (Bingi et al. 1999) and empirical (Hwang et al. 2004;

and Harris, 1997; Akkermans and Helden, 2002), which approved the

contribution of this factor in the adoption of new technology.

109

Compatibility with partners has not been discussed by prior researchers as a

key factor in the adoption of data warehouse technology. On the other hand,

this factor can no longer be ignored and must be considered seriously. In our

highly competitive market the company incorporates with a group of upstream

and down stream partners. To earn an advantage from such a relation, it is a

must to create a common harmony among the supply chain members in

systems, operations, sharing applications and information and so on, to

maximize the overall performance of the supply chain.

The results from this study revealed that compatibility with partners doesn’t

have an effect in the process of data warehouse adoption in Finnish

companies.

The reason might be the lack of coherent understanding about the great

contribution of data warehouse technology, as a tool for storage, multi-

dimensional analyses and reporting internally and externally generated data in

SC matters. By deploying compatible data warehouse among the SC

members, the data can be reached more easily by the partners. In other words,

if the data warehouse system in a focal company is not compatible with the

system adapted by direct partners, then the data flow between the two

companies will be influenced negatively. For more clarity read the following

example: if a focal company has adapted a Datuim DW solution and the direct

partner uses SAP as an ERP solution, then it might happen that both systems

do not integrate and talk to each other easily. As a result, the flow of

information would be greatly affected in negative way, which decreases the

performance of overall SC.

5.7.7.3 Discussion about the Findings in the project-related dimension

Many prior research papers, both theoretical (Nah et al., 2001; Bingi et al.,

1999) and empirical (Wixom and Watson, 2001; Watson et al., 2002; Hwang

et al., 2004; Mabert et al., 2001; Hurley and Harris, 1997; Akkermans and

Helden, 2002; Umbel et al., 2003; Parr and Shanks, 2000), have indicated the

110

major contribution of the existence of a skillful project team factor in adopting

data warehouse technology.

In this study, the results indicated that the skills of project team factor has a

great and positive impact in the adoption of data warehouse projects in

Finnish companies. This factor was ranked as the third most important factor

among the investigated factors.

As known, data warehouse projects are time-consuming, expensive and risky

projects. Therefore, the availability of sufficient resources may reduce the

obstacles during the implementation of data warehouse projects and facilitate

the adoption process, as cited in the earlier research, both theoretical (Bingi et

al., 1999) and empirical (Wixom and Watson, 2001; Hwang et al., 2004;

Akkermans and Helden, 2002).

The empirical results supported the belief constructed by prior research. The

availability of enough resources was ranked as fourth among the investigated

factor in the thesis.

The user involvement in data warehouse project has a great impact in terms of

defining the actual needs and expectations of the project. Also the project

manger can use their knowledge and expertise according to their functional

areas. Since the users are the people who will interact with new system and

use the data, they can draw clear pictures about their expectations regarding

required data. Identifying needed data can be done by defining its

characteristics, meanings, usefulness and relationship with other data, as

discussed by the previous research, both theoretical (Bingi et al., 1999;

Mukherjee and D’Souza, 2003; Solomon, 2005) and empirical (Wixom and

Watson, 2001), (Hwang et al. 2004), (Watson et al. 2002).

In this study, the results indicated that the user involvement has a good impact

in the process of adopting data warehouse technology in Finnish companies.

This factor was ranked seventh among all the investigated factors.

111

Prior research papers highlighted the importance of outside consultants’

participation in new technology projects. The company can use the

consultants’ knowledge and expertise to smooth the implementation process

and reduce unnecessary barriers, as mentioned in the empirical (Hwang et al.

2004), (Mabert et al. 2001), (Akkermans and Helden, 2002) and theoretical

research (Mukherjee and D’Souza, 2003), (Bingi et al. 1999), (Parr and

Shanks, 2000).

This study has concluded that the existence of outside consultants has a good

impact on the adoption of data warehouse technology in Finnish companies.

This factor was ranked sixth among all the investigated factors.

5.7.7.4 Findings and observations of the current status related to the adoption of

the data warehouse technology:

In this part of the analysis, the companies are further investigated utilizing the

data gained from the first part of the questionnaire (which is general data

about the respondents and their companies). The cross-tab tables are used to

study and deeply analyze these data. This part builds increased knowledge

toward the current status related to the adoption of data warehouse technology

in Finnish companies.

The discussion below presents the observations and related explanations

concerning the current status of data warehouse technology in the sample.

Observation 1:

The more matured data warehouse technologies are possessed between

the four-walls of larger companies in the Finnish market.

The table below indicates the data of the last year’s turnover and the year of

installing data warehouse technology.

112

Last-year turnover Year of installation

1991 1999 2000 2001 2002 2003 2004 2005 Cont.

25000000€ -100000000€ 1

100000000€-500000000€ 1 2 1 2 2

500000000€-1000000000€ 3 1

More than 1000000000€ 1 1 1 2

Table 5.5

Moving from top to bottom through the table, the experience in data

warehouse aspects is rising. The logical reason is that the larger companies are

the more competent to adapt expensive technology before the others due to the

availability of sufficient resources and capacities.

As noticed in the third and fourth classifications, the year of installation seems

to be a bit confusing. The companies, which installed their data warehouses in

2003 and 2004 and have continuous development of data warehousing under

the fourth and third classifications respectively had previous data warehouse

systems before the current ones. These companies moved to the systems,

which are better suited to there needs. This observation is based on

respondents’ answers to the question number 7 in the questionnaire, which

explores the reason of changing the previous data warehouse. After reviewing

the current suppliers of the aforementioned companies, 3 out of 4 have

installed SAP data warehousing solutions and the last one has installed a mix

of data warehousing solutions (their data warehouse was assembled from

different suppliers).

As a result, the larger companies in the Finnish market have more experience

in data warehousing and most likely have changed their data warehouse

solutions to better ones.

Observation 2:

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The larger companies in the Finnish market have adopted their data

warehousing solutions from the biggest IT-solution providers for

companies, such as (SAP, Oracle, PeopleSoft …)

Last-year turnover Vendor’s name

self

made

E-big Cognos Datium Oracle SAP mix

25000000€ - 100000000€ 1

100000000€ - 500000000€ 1 2 1 3 1

500000000€ - 1000000000€ 1 3

More than 1000000000€ 3 2

Table 5.6

As observed from the table, the larger companies in the sample have installed

SAP data warehousing solutions (which are considered the biggest software

provider for companies around the globe). The possible reasons, for this high

demand, are the wide range of functionalities and adoption of the so-called,

“best practices,” of doing the core work of data warehousing, provided by

SAP data warehouse solutions (Hashmi, 2000). In the forth classification two

companies have installed their data warehouse technology from different IT

providers. On one hand, this technique is better for companies to meet their

needs and requirements. On the other hand, this technique demands many

resources and much experience to interface the different software with one

another and to apply the future modifications (next versions) to them.

The results from the above discussion can be summarized into three points:

• Larger Finnish companies intend to adopt their data warehouse from

the most dominant IT-solution providers in the globe (SAP or Oracle)

or adopt mixed data warehouse solutions.

• Adopting data warehouse technology from those two sources is more

expensive than adopting it from other sources (less dominant IT-

solution providers).

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• The reason behind the high pricing of a data warehouse supplied from

those two sources is the extra functionality provided by those data

warehouses.

Therefore, we can conclude that larger companies are more willing to spend

additional resources on acquiring their data warehouse technologies.

Observation 3:

Larger companies in the Finnish markets intend to build their data

warehouse applications in phases.

The table below illustrates the sizes of the companies measured by the last

year’s revenue and the data warehouse types.

Last-year turnover Data warehouse types

Department-wide Enterprise-wide

25000000€ - 100000000€ 1

100000000€ - 500000000€ 2 6

500000000€ - 1000000000€ 4

More than 1000000000€ 2 3

Table 5.7

As mentioned in section 5.6.1.1.7 regarding the data warehouse types, 78% of

the sample installed enterprise-wide data warehouses. Based on analyzing the

above table, 40% of the companies who had more than 1000000000€ in

revenue as of last year installed department-wide data warehouses.

After reviewing the year of installation and name of the data warehouse

vendor for those two companies, the results revealed that those companies

have installed SAP and mix of data warehousing applications in 2004 after

changing their previous data warehouse technology. It seems that those

companies intended to implement the phased approach of data warehouse

implementation by adapting department-wide data warehousing. Through

implementing the phased approach, the larger adopters can gain better

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experience for further data warehousing implementation (enterprise-level). In

addition to overcoming the high probability of failure in the implementation

that may appear by utilizing the big bang technique (implementing the system

all at once).

It is better to adopt the phased approach with expensive systems, especially if

the adopters are large-sized companies where the costs of adoption is double

or triple the costs paid by the other ones(O’Leary, 2000).

Observation 4:

The larger companies in the Finnish market consider the data warehouse

project as a complex project.

Last-year turnover Degree of complexity

Not

complex

Weakly

complex

Quite

complex

Complex Very

complex

25000000€ - 100000000€ 1

100000000€ - 500000000€ 1 3 3 1

500000000€ - 1000000000€ 2 2

More than 1000000000€ 3 2

Table 5.8

Moving from top to bottom through the table, the complexity of data

warehouse project is growing. For the reasons that follow, the complexity of

data warehouses in larger companies was evaluated highly:

• Realizing larger set-up and ongoing costs to get the job done

• The need for a longer implementation period to install the system

• Involving a larger workforce to complete the system.

5.8 Summary of the chapter In this chapter, research objectives, research model, hypotheses, data

collection methods and techniques, data analysis and observations from the

research were cited and discussed.

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The response rate was 8%. The data collected through the questionnaire from

18 companies in Finland was analyzed through descriptive statistics and

analytical tables.

The ranked-list of critical success factors was presented. The results from the

aforementioned list revealed that factors such as top management sponsorship,

champions, skillful project team, availability and coordination of resources,

business internal needs, the existence of outside consultants, end-user

involvement, and selection of vendors would affect the adoption of data

warehouse technology in Finnish companies.

Finally, the observations of the current status related to the adoption of data

warehouse technology were carried out and discussed.

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6. Conclusion

6.1 Objective and structure This chapter aims to outline the conclusions of this study, and the implications

for further research.

Section 6.2 presents the general conclusions of the thesis. In section 6.3 the

validity, reliability and generalizability of this study are indicated and

described. Finally, section 6.4 introduces the possible propositions for future

research on data warehouses.

6.2 General Conclusions This section is divided into three parts:

6.2.1 Conclusions about the critical success factors of data warehousing in

Finnish companies

Data warehouse technology is a powerful tool to overcome data-related

obstacles and enhance decision making initiatives in our highly globalized and

competitive market.

A data warehouse solution is not only a software package. It is a complex

process to establish sophisticated and integrated information systems. The

adoption of this technology requires massive capital expenditure, utilizes a

certain deal of implementation time and has a very high likelihood of failure.

Therefore, many adoption-related factors must be carefully assessed before

the real adoption is actualized.

The results from this study revealed that all organizational and project-related

factors, and one factor under the environmental dimension (vendor selection)

are important considerations for Finnish organizations.

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Specifically, these factors include top management sponsorship, existence of

champions, a skillful project team, availability of resources, company internal

needs, support from outside consultants, end-user involvement, and vendor

selection.

The results revealed, as well, that these factors influence the success of data

warehousing in pre-implementation and implementation phases.

No wonder that the Top management sponsorship got the highest percentage

among all the investigated factors. If the high-level management supports the

adoption of data warehouse, then needed resources will be obtained.

The existence of a champion is considered one of the most important factors

effecting the adoption of data warehouse technology. Champions play a vital

task in persuading the staff to see their own personal visions to adopt new

technology and secure required capital and information.

Having a proficient project team may effect largely the smooth progression of

the data warehouse adoption project.

Data warehouse technology is an expensive and risky undertaking. Therefore

securing required resources is important to continue this project and make the

new technology come to life.

This study believes that the internal needs of a company have a great

influence on the decision to adopt data warehouse technology or not.

With outside consultants the company can go on easily in the project and meet

its expectations. Companies hire the consultants, who are knowledgeable

about new technology, to overcome the lack of knowledge about new

technology amongst the in-house staff.

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Involving the end-users in the data warehouse project has an endless impact

on promoting the vision of adopting this technology. Understanding users’

needs and expectations and trying to meet them lead to reduced resistance and

increased acceptance of the new technology.

This study believes that the company cannot accept and rely fully on the

suggestions and plans given by the vendors. Therefore, careful consideration

must be paid when selecting a data warehouse supplier (implementation

partner).

The data has supported the first eight factors as critical success issues to be

considered by high-level managers when adopting a data warehouse

technology in Finnish companies.

Compatibility with partners’ information systems and the degree of business

competition were considered as non-key issues that influence the adoption of

data warehouse technology in Finnish adopters.

6.2.2 Conclusions about the benefits obtained from installing data warehouse

applications

Theoretically, any organization that adapts and sustains a data warehouse

correctly will realize payoffs.

Hard benefits can be achieved through cost savings, increased revenue and

raised quality of marketing analysis.

Soft benefits can be measured by the technology’s effect on the user. By

securing faster access to more accurate and reliable data the user can better

serve their clients.

Empirically, the results from practical research have indicated that data

warehouse technology is an important element to boost the productivity value

in Finnish adopters.

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On the other hand, values such as product profitability, customer profitability,

employee profitability, branch profitability, and customer satisfaction

wouldn’t be affected by adapting data warehouse technology in Finnish

companies.

6.2.3 Conclusions about the current status related to the adoption of data

warehouse technology

Based on performing a cross-tab analysis on the data gathered from the first

part of the questionnaire, the following conclusions can be highlighted in

regard of the current status of data warehouse technology in the investigated

companies.

1. Larger companies in the Finnish market possess mature data warehouse

technology because they are capable of adopting this technology before

others.

2. The larger Finnish companies adopted their data warehouse solutions from

the biggest IT-solution provider (SAP) or adapted a mixed solution of data

warehousing from different data warehouse providers.

The data warehouse supplied from those two sources is more expensive

than the data warehouse supplied from other sources (from other data

warehouse solution providers).

3. The larger Finnish companies intend to build their data warehouse

technology utilizing the phased approach. The reasons might be to reduce

the probability of failure in the implementation and increase their expertise

for advanced data warehouse implementation.

4. The larger Finnish companies consider a data warehouse project to be a

very complex project.

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6.3 Validity, reliability and generalizability

The research instrument in this study is a questionnaire sent to 220 companies

in Finland. This instrument was assessed for its reliability, validity as well as

generalizability.

The response rate was about 8% (18 responses). As known, a larger response

rate is associated with a stronger validity in research. This rate was normal,

based on the reasons cited in the section 5.6.1. This should not significantly

affect the research findings, especially for the convenience of explanation and

testing proposed hypotheses.

In terms of validity and reliability of the research instrument, a three-round

process of revision was formulated.

The questionnaire was checked by my supervisor Mr. Anders Tallberg to

review each question and make necessary modifications. Then, the

questionnaire was sent and further reviewed by a panel of PHD students.

Finally, the questionnaire got the approval from Mr. Anders Tallberg after his

second assessment and review.

As for the generalizability of the study, although this study reports good

empirical data on critical success factors influencing the adoption of data

warehouses for Finnish companies, the results are seemed to be difficult to be

generalized. One logical reason is that, it is difficult to statistically test the

significance of the hypotheses with 18 responses in hand. Therefore, the

results and conclusions from this study can not be generalized for the entire

population.

6.4 Implications for further research

This study provides a good insight concerning the investigation of factors

effecting the adoption of data warehouse technology in Finnish companies.

The next and normal step will be to introduce the key issues, which effect

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effective and efficient stability of data warehouse technology in adopters. In

addition to knowing the best ways to integrate this emerging technology with

other technologies such as ERP systems, SCM systems, CRM systems, in

order to heighten the overall performance of companies and maximize profit.

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References

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Diego, 2001.

2. N. Hashmi, Business Information Warehouse for SAP, 2000, Prima Tech.

3. W.H. Inmon, Building the data warehouse, Wiley, New York, 1996.

4. R. Kimball, The data warehouse Toolkit, Wiley, New York, 1996.

5. D. O’Leary, Enterprise resource planning systems, Cambridge Press, New York,

2000.

6. C. Todman, Designing a data warehouse, Prentice Hall PTR, New Jersey, 2001.

2. E-articles:

1. Akkermans and Helden, Vicious and virtuous cycles in ERP implementation: A case

study of interrelations between critical success factors, European journal of

information systems, 2002, Vol.11 Iss. 1, p35.

2. Bingi et al., Critical issues affecting an ERP implementation, Information systems

management, 1999, Vol.16 Issue3.

3. V. Gupta, An introduction to Data warehousing, System services corporation, 1997.

4. Hurley Harris, Facilitating corporate knowledge: building the data warehouse,

Information management & computer security, 1997, p170.

5. Hwang et al., Critical factor influencing the adoption of data warehouse technology:

A study of the banking industry in Taiwan, Decision support systems, 2004, p1.

6. Joshi and Curtis, Issues in building a successful data warehouse, the executive’s

journal, 1999, Vol.15 Issue 2, p28.

7. Ken Orr, Data warehousing technology, Ken Orr institute, 2000.

8. Mabert et al., Enterprise Resource Planning: Common Myths versus Evolving

Reality, Business Horizons, 2001, Vol.44 issue 3, p69.

9. Mukherjee and D’Souza, Think phased implementation for successful data

warehousing, information systems management, 2003, p82.

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10. Nah et al., Critical factors for successful implementation of enterprise systems,

Business process management, Bradford: 2001, Vol.7 Iss.3, p285.

11. Parr and Shanks, A model of ERP project implementation, Journal of information

technology, 2000, p289.

12. A. Smith, Data warehousing & ERP… A combination of forces, The data

administration Newsletter (TDAN.com).

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management, 2005, Vol. 22 Iss. 1, p26.

14. Umble et al., Enterprise resource planning: Implementation procedures and critical

success factors, European journal of operational research, 2003, p241.

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exceptional payoffs, Information & management, 2002, Vol.39 Iss.6, p491.

3. Websites:

1. http://www.mnhs.org/preserve/records/dwintro.html

2. http://www.databasejournal.com/sqletc/article.php/1457041

3. http://www.tdwi.org/

4. http://www.infogoal.com/dmc/dmcdwh.htm

5. http://www.tdan.com

6. http://www.bitpipe.com/data/search?site=bp&qp=site_abbrev%3Abp&qg

=VENDOR&cr=bpres&cp=bpres&st=1&rp=1&oq=datawarehouse&sw=0

&qt=data+warehousing&Search.x=22&Search.y=9

7. http://www.dwinfocenter.org/

8. http://www.datawarehouse.com/index.cfm

9. http://www.ciol.com/content/e_ent/data_ware/

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Appendix

Questionnaire

Objectives and Definitions:

The objective of this questionnaire is to build a comprehensive understanding of the

critical success factors, which influence the data warehouse technology in Finnish

companies.

Data warehouse is a huge data repository or database which collects data from

different data sources and then accumulates, and stores them in one place for further

analysis, prediction and decision making initiatives.

Enterprise-wide data warehouse: The data in this type of data warehouse is

enterprise-level data (example, the amount of sales for the overall organization) and

collected from different data sources across the enterprise.

Department-level data warehouse: The data is department-level data (example, the

amount of sales for the entire department) and collected from different data sources

across the department

Section 1: Company-related questions:

1. Title of post of the respondent:

2. Company size (number of employees):

3. Last year revenue:

4. The type of industry in which the company incorporates:

5. When was the data warehouse installed?

6. Name of the vendor of your current data warehouse system:

7. Was there any previous system installed? Why have you changed it?

8. What type of data warehouse do you have? (Enterprise-wide or department-level

data warehouse)

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9. In terms of complexity, can you define how complex is the data warehouse

implementation project? (very complex, complex, quite complex, weakly

complex, not complex, N/A)

Section 2: Subject-related questions:

Please indicate how important you think the following factors were for the

successful implementation of the data warehouse in your company:

1- Not important. 2- Weakly Important. 3- Quite important. 4- Important.

5- Very important. N/A

1. Champions (people inside the organization who drive and advocate the adoption of

the new technology). 1.2.3.4.5.N/A

2. Top management sponsorship (support and approval of the data warehouse project

from the top management of the company). 1.2.3.4.5.N/A

3. Business internal needs (that the data warehouse fills a perceived need for

improvement of business operations). 1.2.3.4.5.N/A

4. Vendors (the suppliers of the required software, hardware, perhaps also the

implementation team and the plans for the data warehouse project). 1.2.3.4.5.N/A

5. End-users involvement (the participation of the end users in the data warehouse

project). 1.2.3.4.5.N/A

6. Consultants (experts in data warehouse technology from outside the organization).

1.2.3.4.5.N/A

7. The business competition affects the data warehouse adoption especially if the

competitors are adopting or have adopted this technology? 1.2.3.4.5.N/A

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8. The company interacts with a group of partners (suppliers and customers). Does the

compatibility with the partners systems affect the selection and successful adoption of

the data warehouse technology? 1.2.3.4.5.N/A

9. The diversity of skills and the background of the project team influence the successful

adoption of the data warehouse technology. 1.2.3.4.5.N/A

10. The resources available (money, time, and people), coupled with efficient

cooperation and use, have a critical impact on the data warehouse adoption?

1.2.3.4.5.N/A

11. According to your observations, the data warehouse technology has led to changes in:

• Product profitability 1.2.3.4.5.N/A

• Customer profitability 1.2.3.4.5.N/A

• Employee profitability 1.2.3.4.5.N/A

• Branch profitability 1.2.3.4.5.N/A

• Productivity (Efficiency in the business process i.e. less effort and money

consumed) 1.2.3.4.5.N/A

• Customer satisfaction 1.2.3.4.5.N/A

Thank you for your time! Your participation is greatly appreciated.